diff --git a/docs/_includes/docs-healthcare-pagination.html b/docs/_includes/docs-healthcare-pagination.html
index 510272bae6..7156be5092 100644
--- a/docs/_includes/docs-healthcare-pagination.html
+++ b/docs/_includes/docs-healthcare-pagination.html
@@ -10,6 +10,7 @@
     </li>
 </ul>
 <ul class="pagination owl-carousel pagination_big">
+    <li><a href="release_notes_5_4_0">5.4.0</a></li>
     <li><a href="release_notes_5_3_3">5.3.3</a></li>
     <li><a href="release_notes_5_3_2">5.3.2</a></li>
     <li><a href="release_notes_5_3_1">5.3.1</a></li>
diff --git a/docs/en/licensed_version_compatibility.md b/docs/en/licensed_version_compatibility.md
index d239ea74e7..da1cc3504e 100644
--- a/docs/en/licensed_version_compatibility.md
+++ b/docs/en/licensed_version_compatibility.md
@@ -16,6 +16,7 @@ sidebar:
 {:.table-model-big}
 | Spark NLP for Healthcare	| Spark NLP (Public) |
 |---------------------------|--------------------|
+| 5.4.0                     | 5.4.0              |
 | 5.3.3                     | 5.3.2              |
 | 5.3.2                     | 5.3.2              |
 | 5.3.1                     | 5.3.1              |
diff --git a/docs/en/spark_nlp_healthcare_versions/licensed_release_notes.md b/docs/en/spark_nlp_healthcare_versions/licensed_release_notes.md
index 82c7512c9b..4d03fea275 100644
--- a/docs/en/spark_nlp_healthcare_versions/licensed_release_notes.md
+++ b/docs/en/spark_nlp_healthcare_versions/licensed_release_notes.md
@@ -5,7 +5,7 @@ seotitle: Spark NLP for Healthcare | John Snow Labs
 title: Spark NLP for Healthcare Release Notes
 permalink: /docs/en/spark_nlp_healthcare_versions/licensed_release_notes
 key: docs-licensed-release-notes
-modify_date: 2024-05-21
+modify_date: 2024-07-10
 show_nav: true
 sidebar:
     nav: sparknlp-healthcare
@@ -13,842 +13,1020 @@ sidebar:
 
 <div class="h3-box" markdown="1">
 
-## 5.3.3
+## 5.4.0
 
 #### Highlights
 
-We are delighted to announce remarkable enhancements and updates in our latest release of Spark NLP for Healthcare. **This release comes with a brand new PipelineTracer module to return structured jsons from pretrained pipelines, brand new hyperparameters to customize relation extraction models and Deidentification process, and 22 new clinical pretrained models and pipelines**. 
-
-+ Introducing 7 new Sentence Entity Resolver Models for entity mapping to medical terminologies, using SOTA `BioLord` sentence embeddings
+We are delighted to announce remarkable enhancements and updates in our latest release of Spark NLP for Healthcare. **This release comes with a brand new LLM Loader to allow any size of LLMs in gguf format, a few-shot assertion classifier, contextual assertion detection and a demo to showcase accuracy differences between Healthcare NLP and GPT-4 for information extraction tasks as well as the first Menopause-specific medical models, 82 new and updated clinical pretrained models, and pipelines**.
+ 
++ Introducing a brand new `LLMLoader` annotator to load and run large language models in gguf format. We also announce **9 LLMs at various sizes and quantizations** (3x small size medical summarizer and QA model, 3x medium size general model, and 3x small size zero shot entity extractor)
++ Introducing a brand new `FewshotAssertionClassifier` annotator to train assertion detection models using a few samples with better accuracy 
++ Introducing a rule-based `ContextualAssertion` annotator to detect assertion status using patterns and rules without any training or annotation
++ Introducing `VectorDBPostProcessor` annotator to filter and sort the document splits returned by vector databases in a RAG application
++ Introducing `ContextSplitAssembler` annotator to assemble the document post-processed splits as a context into an LLM stage in a RAG application
++ SNOMED entity resolver model for `Veterinary` domains
++ Voice of the Patients named entity recognition (NER) model
++ New rule-based entity matcher models to customize De-IDentification pipelines
++ New NER, assertion, relation extraction, and classification models to identify `Alcohol` and `Smoking` related Medical Entities
++ New NER and assertion models to extract `Menopause` related entities
 + Clinical document analysis with one-liner pretrained pipelines for specific clinical tasks and concepts
-+ Introducing 2 new Chunk Mapper models designed for medical code mapping between `SNOMED` and `MedDRA` terminologies
-+ Improved version of Social Determinants of Health (SDoH) named entity recognition model with reduced set of core entities
-+ Automating pipeline tracing and analysis with `PipelineTracer`  to help return structured jsons from pretrained pipelines via the `OuputParser` module
-+ Configuring age-based obfuscation with the `setAgeGroups` parameter
-+ Enhancing date obfuscation control with the `setKeepYear` parameter in the `Deidentification` annotator to allow `year` info intact
-+ Broadening relation extraction with extended scope windows, `directionSensitive` and `filterByTokenDistance` parameters to allow further customization and reduce FPs
-+ Enhancing rule-based annotators with the `ner_source` field for improved chunk tracking and prioritization
-+ Introduction of a new parameter `dataSetInfo` to store dataset details for `AssertionDL` and `GenericClassifier` for traceability
-+ Converting visual NER annotations to CoNLL format for training text-based NER models with visual annotations
-+ Performance analysis of deidentification pipelines on clinical texts in a cluster environment
-+ New blogposts on relation extraction, MedDRA response to treatment, and pretrained pipelines.
++ Formal release of oncological assertion status detection and relation extraction models
++ 11 new fine-tuned sentence embedding models finetuned with medical assertion datasets
++ Significantly faster vector-db based entity resolution models than existing Sentence Entity Resolver models
++ RxNorm code mapping benchmarks and cost comparisons: Healthcare NLP, GPT-4, and Amazon Comprehend Medical  
++ New blog posts on using NLP in opioid research and healthcare: harnessing NLP, knowledge graphs, and regex techniques for critical insights
++ New notebooks for medication and resolutions concept
++ Updated Udemy MOOC (our online courses) notebooks
 + Various core improvements; bug fixes, enhanced overall robustness and reliability of Spark NLP for Healthcare
-    - Added training params to trainable annotators within the metadata of the trained models
-    - Updated Risk Adjustment module with V28Y24
-    - Resolved index issue in `AssertionChunkConverter` annotator and `AnnotationLab.get_assertion_data` modules
-    - Resolved saving issue in `Flattener` annotator
+    - Resolved broken links in healthcare demos
+    - Added a unique ID field for each entity  into the result of the `pipeline_ouput_parser` module
+    - Fixed deidentification AGE obfuscation hanging issue
+    - Added DatasetInfo parameter into the `MedicalNERModel` annotator
 + Updated notebooks and demonstrations for making Spark NLP for Healthcare easier to navigate and understand
-    - New [PipelineTracer and PipelineOutputParser Notebook](https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/11.4.PipelineTracer_and_PipelineOutputParser.ipynb)
-    - Updated [Task Based Clinical Pretrained Pipelines Notebook](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/11.3.Task_Based_Clinical_Pretrained_Pipelines.ipynb)
-    - Updated [Pretrained Clinical Pipelines Notebook](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/11.Pretrained_Clinical_Pipelines.ipynb)
-    - Updated [ADE Demo](https://demo.johnsnowlabs.com/healthcare/ADE/)
-    - Updated [NER_POSOLOGY Demo](https://demo.johnsnowlabs.com/healthcare/NER_POSOLOGY/)
-    - Updated [NER_RADIOLOGY Demo](https://demo.johnsnowlabs.com/healthcare/NER_RADIOLOGY/)
-    - Updated [VOP Demo](https://demo.johnsnowlabs.com/healthcare/VOP/)
-    - Updated [SDOH Demo](https://demo.johnsnowlabs.com/healthcare/SDOH/)
-    - Updated [ONCOLOGY Demo](https://demo.johnsnowlabs.com/healthcare/ONCOLOGY/)
+    - New [Clinical Medication Use Case](https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/43.Clinical_Medication_Use_Case.ipynb) notebook
+    - New [Resolving Medical Terms to Terminology Codes Directly](https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/3.4.Resolving_Medical_Terms_to_Terminology_Codes_Directly.ipynb) notebook
+    - New [Contextual Assertion](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/2.3.Contextual_Assertion.ipynb) notebook
+    - New [VectorDB and PostProcessor for RAG Generative AI](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/45.VectorDB_and_PostProcessor_for_RAG_Generative_AI.ipynb) notebook
+    - New [Analyse Veterinary Documents with Healthcare NLP](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/44.Analyse_Veterinary_Documents_with_Healthcare_NLP.ipynb) notebook
+    - Updated [FewShot Assertion Classifier](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/2.2.FewShot_Assertion_Classifier.ipynb) notebook
+    - New [ALCOHOL SMOKING](https://demo.johnsnowlabs.com/healthcare/ALCOHOL_SMOKING/) Demo
+    - New [JSL vs GPT4](https://demo.johnsnowlabs.com/healthcare/JSL_GPT4/) Demo to showcase accuracy differences between Healthcare NLP and GPT-4 for information extraction tasks
 + The addition and update of numerous new clinical models and pipelines continue to reinforce our offering in the healthcare domain
 
 These enhancements will elevate your experience with Spark NLP for Healthcare, enabling more efficient, accurate, and streamlined analysis of healthcare-related natural language data.
 
-</div><div class="h3-box" markdown="1">
-
-
-####  Introducing 7 New Sentence Entity Resolver Models for Entity Mapping to Medical Terminologies, Using SOTA `BioLord` Sentence Embeddings
-
-The latest lineup of 7 cutting-edge resolver models are designed to enhance clinical entity mapping and coding accuracy. These models leverage advanced natural language processing to seamlessly map medical entities and concepts to standardized codes, facilitating streamlined data analysis and healthcare decision-making. Each model specializes in precise code assignment within specific medical domains, from drug ingredients to veterinary-related entities. Dive into our resolver models and empower your applications with state-of-the-art clinical entity resolution.
 
-{:.table-model-big}
-| Model Name                                                            |      Description            |
-|-----------------------------------------------------------------------|-----------------------------|
-| [`sbiobertresolve_umls_general_concepts`](https://nlp.johnsnowlabs.com/2024/05/06/biolordresolve_umls_general_concepts_en.html) | This model maps clinical entities and concepts to the following 4 UMLS CUI code categories |
-| [`biolordresolve_umls_general_concepts`](https://nlp.johnsnowlabs.com/2024/05/06/biolordresolve_umls_general_concepts_en.html) | This model maps clinical entities and concepts to the following 4 UMLS CUI code categories |
-| [`biolordresolve_icd10cm_augmented_billable_hcc`](https://nlp.johnsnowlabs.com/2024/05/06/biolordresolve_icd10cm_augmented_billable_hcc_en.html) | This model maps extracted medical entities to ICD-10-CM codes |
-| [`biolordresolve_avg_rxnorm_augmented`](https://nlp.johnsnowlabs.com/2024/05/07/biolordresolve_avg_rxnorm_augmented_en.html) | This model maps clinical entities and concepts (like drugs/ingredients) to RxNorm codes |
-| [`biolordresolve_snomed_findings_aux_concepts`](https://nlp.johnsnowlabs.com/2024/05/07/biolordresolve_snomed_findings_aux_concepts_en.html) | This model maps clinical entities and concepts to SNOMED codes |
-| [`biolordresolve_cpt_procedures_measurements_augmented`](https://nlp.johnsnowlabs.com/2024/05/08/biolordresolve_cpt_procedures_measurements_augmented_en.html) | This model maps medical entities to CPT codes |
-| [`sbiobertresolve_snomed_veterinary_wip`](https://nlp.johnsnowlabs.com/2024/05/06/sbiobertresolve_snomed_veterinary_wip_en.html) | TThis model maps veterinary-related entities and concepts to SNOMED codes |
+</div><div class="h3-box" markdown="1">
+ 
+#### Introducing a Brand New `LLMLoader` Annotator to Load and Run Large Language Models in GGUF format
 
+`LLMLoader` is designed to interact with a LLMs that are converted into gguf format. This module allows using John Snow Labs' licensed LLMs at various sizes that are finetuned on medical context for certain tasks. It provides various methods for setting parameters, loading models, generating text, and retrieving metadata. The `LLMLoader` includes methods for setting various parameters such as input prefix, suffix, cache prompt, number of tokens to predict, sampling techniques, temperature, penalties, and more. Overall, the `LLMLoader`  provides a flexible and extensible framework for interacting with language models in a Python and Scala environment using PySpark and Java.
 
 *Example*:
 
 ```python
-icd10cm_resolver = SentenceEntityResolverModel.pretrained("biolordresolve_icd10cm_augmented_billable_hcc", "en", "clinical/models") \
-    .setInputCols(["sentence_embeddings"])\
-    .setOutputCol("icd10_code")\
-    .setDistanceFunction("EUCLIDEAN")
 
-text = "John Doe, a 49-year-old male with CMT2P, AIDS-causing virus infection, and PKD2, presents for a follow-up visit to manage his chronic conditions."
+from sparknlp_jsl.llm import LLMLoader
+
+llm_loader_pretrained = LLMLoader(spark).pretrained("JSL_MedS_q16_v1", "en", "clinical/models")
+
+llm_loader_pretrained.generate("What is the indication for the drug Methadone?")
 ```
 
 *Result*:
 
-{:.table-model-big}
-|chunk|sbiobert icd10cm code|sbiobert icd10cm resolution|biolord icd10cm code|biolord icd10cm resolution|
-|-|-|-|-|-|
-|CMT2P|G12.1|sma2 [other inherited spinal muscular atrophy]|G60.0|cmt2p - charcot-marie-tooth disease type 2p [hereditary motor and sensory neuropathy]|
-|AIDS-causing virus infection|B34.9|disease caused by virus [viral infection, unspecified]|B20|hiv - human immunodeficiency virus infection [human immunodeficiency virus [hiv] disease]|
-|PKD2|C77.9|pn2 category [secondary and unspecified malignant neoplasm of lymph node, unspecified]|Q61.2|pkd2 - polycystic kidney disease 2 [polycystic kidney, adult type]|
+```bash
+Methadone is used to treat opioid addiction. It is a long-acting opioid agonist that is used to help individuals who are addicted to short-acting opioids such as heroin or other illicit opioids. It is also used to treat chronic pain in patients who have developed tolerance to other opioids.
+```
 
 
 
 </div><div class="h3-box" markdown="1">
 
-####  Clinical Document Analysis with One-Liner Pretrained Pipelines for Specific Clinical Tasks and Concepts
+#### Introducing a Brand New `FewshotAssertionClassifier` Annotator to Train Assertion Detection Models Using a Few Samples with Better Accuracy 
 
-We introduce a suite of advanced, hybrid pretrained pipelines, specifically designed to streamline the clinical document analysis process. These pipelines are built upon multiple state-of-the-art (SOTA) pretrained models, delivering a comprehensive solution for quickly extracting vital information.
+The newly refactored `FewShotAssertionClassifierModel` and `FewShotAssertionClassifierApproach` simplify assertion annotation in clinical and biomedical texts. By leveraging sentence embeddings, these models deliver precise assertion annotations and integrate seamlessly with any SparkNLP sentence embedding model.
 
-What sets this release apart is the elimination of complexities typically involved in building and chaining models. Users no longer need to navigate the intricacies of constructing intricate pipelines from scratch or the uncertainty of selecting the most effective model combinations. Our new pretrained pipelines simplify these processes, offering a seamless, user-friendly experience.
+A key feature is the `FewShotAssertionSentenceConverter`, an annotator that formats documents/sentences and NER chunks for assertion classification, requiring an additional step in the pipeline.
+
+This comprehensive approach significantly enhances the extraction, analysis, and processing of assertion-related data, making it an indispensable tool for healthcare text annotation.
+
+The following table demonstrates the enhanced results achieved using the FewShot Assertion model compared to the traditional AssertionDL model across various datasets. The FewShot Assertion model showcases significant improvements in accuracy scores, particularly in complex medical domains.
+
+| Dataset Name  | AssertionDL  | FewShot Assertion |
+|---------------|--------------|-------------------|
+| radiology     | 0.91         | 0.93              |
+| i2b2          | 0.86         | 0.93              |
+| oncology      | 0.55         | 0.90              |
+| jsl_augmented | 0.85         | 0.90              |
+| smoking       | 0.67         | 0.96              |
+| sdoh          | 0.76         | 0.85              |
+
+![image](https://github.com/JohnSnowLabs/spark-nlp-internal/assets/64752006/9d2e4d5d-b727-45c4-82b5-1b453e373653)
 
-{:.table-model-big}
-| Model Name                                                            |      Description            |
-|-----------------------------------------------------------------------|-----------------------------|
-| [`explain_clinical_doc_sdoh`](https://nlp.johnsnowlabs.com/2024/05/01/explain_clinical_doc_sdoh_en.html) | This pipeline is designed to extract all clinical/medical entities, assertion status, and relation informations which may be considered as Social Determinants of Health (SDOH) entities from text. |
-| [`explain_clinical_doc_mental_health`](https://nlp.johnsnowlabs.com/2024/05/06/explain_clinical_doc_mental_health_en.html) | This pipeline is designed to extract all mental health-related entities, assertion status, and relation information from text. |
-| [`ner_medication_generic_pipeline`](https://nlp.johnsnowlabs.com/2024/04/25/ner_medication_generic_pipeline_en.html) | This pre-trained pipeline is designed to identify generic `DRUG` entities in clinical texts. It was built on top of the `ner_posology_greedy`, `ner_jsl_greedy`, `ner_drugs_large`, and `drug_matcher` models to detect the entities `DRUG`, `DOSAGE`, `ROUTE`, and `STRENGTH` chunking them into a larger entity as `DRUG` when they appear together. |
-| [`ner_deid_generic_context_augmented_pipeline`](https://nlp.johnsnowlabs.com/2024/05/20/ner_deid_generic_context_augmented_pipeline_en.html) | This pipeline can be used to extract PHI information such as `AGE`, `CONTACT`, `DATE`, `LOCATION`, `NAME`, `PROFESSION`,  `IDNUM`, `MEDICALRECORD`, `ORGANIZATION`, `PHONE`, `EMAIL`, `ACCOUNT`, `LICENSE`, `VIN`, `SSN`, `DLN`, `PLATE`, `IPADDR` entities. |
-| [`ner_deid_subentity_context_augmented_pipeline`](https://nlp.johnsnowlabs.com/2024/05/20/ner_deid_subentity_context_augmented_pipeline_en.html) | This pipeline can be used to extract PHI information such as `AGE`, `CONTACT`, `DATE`, `LOCATION-OTHE`, `PROFESSION`, `CITY`, `COUNTRY`, `DOCTOR`, `HOSPITAL`, `IDNUM`, `MEDICALRECORD`, `ORGANIZATION`, `PATIENT`, `PHONE`, `EMAIL`, `STREET`, `USERNAME`, `ZIP`, `ACCOUNT`, `LICENSE`, `VIN`, `SSN`, `DLN`, `PLATE`, `IPADDR` entities. |
-| [`ner_deid_context_augmented_pipeline`](https://nlp.johnsnowlabs.com/2024/05/20/ner_deid_subentity_context_augmented_pipeline_en.html) | This pipeline can be used to extract PHI information such as `AGE`, `CONTACT`, `DATE`, `LOCATION`, `NAME`, `PROFESSION`, `CITY`, `COUNTRY`, `DOCTOR`, `HOSPITAL`, `IDNUM`, `MEDICALRECORD`, `ORGANIZATION`, `PATIENT`, `PHONE`, `EMAIL`, `STREET`, `USERNAME`, `ZIP`, `ACCOUNT`, `LICENSE`, `VIN`, `SSN`, `DLN`, `PLATE`, `IPADDR` entities. |
 
+|FewShot Assertion Model Name| Predicted Classed |
+|----------------------------|-------------------|
+|[fewhot_assertion_jsl_e5_base_v2_jsl](https://nlp.johnsnowlabs.com/2024/07/03/fewhot_assertion_jsl_e5_base_v2_jsl_en.html) | `Present`, `Absent`, `Possible`, `Planned`, `Past`, `Family`, `Hypothetical`, `SomeoneElse`  |
+|[fewhot_assertion_i2b2_e5_base_v2_i2b2](https://nlp.johnsnowlabs.com/2024/07/03/fewhot_assertion_i2b2_e5_base_v2_i2b2_en.html) | `absent`, `associated_with_someone_else`, `conditional`, `hypothetical`, `possible`, `present`   | 
+|[fewhot_assertion_sdoh_e5_base_v2_sdoh](https://nlp.johnsnowlabs.com/2024/07/04/fewhot_assertion_sdoh_e5_base_v2_sdoh_en.html)|  `Absent`, `Past`, `Present`, `Someone_Else`, `Hypothetical`, `Possible`  | 
+|[fewhot_assertion_smoking_e5_base_v2_smoking](https://nlp.johnsnowlabs.com/2024/07/03/fewhot_assertion_smoking_e5_base_v2_smoking_en.html) | `Present`, `Absent`, `Past`  | 
+|[fewhot_assertion_oncology_e5_base_v2_oncology](https://nlp.johnsnowlabs.com/2024/07/03/fewhot_assertion_oncology_e5_base_v2_oncology_en.html) | `Absent`, `Past`, `Present`, `Family`, `Hypothetical`, `Possible`  | 
+|[fewhot_assertion_radiology_e5_base_v2_radiology](https://nlp.johnsnowlabs.com/2024/07/03/fewhot_assertion_radiology_e5_base_v2_radiology_en.html) | `Confirmed`, `Negative`, `Suspected`| 
 
 *Example*:
 
 ```python
-from sparknlp.pretrained import PretrainedPipeline
+few_shot_assertion_converter = FewShotAssertionSentenceConverter()\
+    .setInputCols(["sentence","token", "ner_jsl_chunk"])\
+    .setOutputCol("assertion_sentence")
 
-pipeline_sdoh = PretrainedPipeline("explain_clinical_doc_sdoh", "en", "clinical/models")
+e5_embeddings = E5Embeddings.pretrained("e5_base_v2_embeddings_medical_assertion_oncology", "en", "clinical/models")\
+    .setInputCols(["assertion_sentence"])\
+    .setOutputCol("assertion_embedding")
 
-text = """The patient reported experiencing symptoms of anxiety and depression, which have been affecting his quality of life. 
-He reported a history of childhood trauma related to violence and abuse in his household, which has contributed to his smoking, alcohol use and current mental health struggles."""
+few_shot_assertion_classifier = FewShotAssertionClassifierModel()\
+    .pretrained("fewhot_assertion_oncology_e5_base_v2_oncology", "en", "clinical/models")\
+    .setInputCols(["assertion_embedding"])\
+    .setOutputCol("assertion")
 
+sample_text= """The patient is suspected to have colorectal cancer. Her family history is positive for other cancers. The result of the biopsy was positive. A CT scan was ordered to rule out metastases."""
 ```
 
-*NER and Assertion Result*:
-
-{:.table-model-big}
-| chunks          |begin |end | entities         | assertion|
-|:----------------|-----:|---:|:-----------------|:---------|
-| anxiety         |   46 | 52 | Mental_Health    | Present  |
-| depression      |   58 | 67 | Mental_Health    | Present  |
-| childhood trauma|  143 |158 | Childhood_Event  | Past     |
-| violence        |  171 |178 | Violence_Or_Abuse| Past     |
-| abuse           |  184 |188 | Violence_Or_Abuse| Past     |
-| smoking         |  237 |243 | Smoking          | Present  |
-| alcohol         |  246 |252 | Alcohol          | Present  |
+*Result*:
 
+|    | chunks            |   begin |   end | entities         | assertion   |   confidence |
+|---:|:------------------|--------:|------:|:-----------------|:------------|-------------:|
+|  0 | colorectal cancer |      33 |    49 | Cancer_Dx        | Possible    |     0.581282 |
+|  1 | cancers           |      93 |    99 | Cancer_Dx        | Family      |     0.234656 |
+|  2 | biopsy            |     120 |   125 | Pathology_Test   | Past        |     0.957321 |
+|  3 | positive          |     131 |   138 | Pathology_Result | Present     |     0.956439 |
+|  4 | CT scan           |     143 |   149 | Imaging_Test     | Past        |     0.95717  |
+|  5 | metastases        |     175 |   184 | Metastasis       | Possible    |     0.549866 ||
 
-*Relation Extraction Result*:
 
-{:.table-model-big}
-| relation type                     | entity1           | chunk1           | entity2           | chunk2          | confidence|
-|:----------------------------------|:------------------|:-----------------|:------------------|:----------------|:----------|
-| Mental_Health-Quality_Of_Life     | Mental_Health     | anxiety          | Quality_Of_Life   | quality of life |      0.98 |
-| Mental_Health-Quality_Of_Life     | Mental_Health     | depression       | Quality_Of_Life   | quality of life |      0.95 |
-| Childhood_Event-Violence_Or_Abuse | Childhood_Event   | childhood trauma | Violence_Or_Abuse | violence        |      0.96 |
-| Childhood_Event-Violence_Or_Abuse | Childhood_Event   | childhood trauma | Violence_Or_Abuse | abuse           |      0.97 |
-| Childhood_Event-Alcohol           | Childhood_Event   | childhood trauma | Alcohol           | alcohol         |      1.00 |
-| Violence_Or_Abuse-Alcohol         | Violence_Or_Abuse | violence         | Alcohol           | alcohol         |      0.99 |
-| Violence_Or_Abuse-Alcohol         | Violence_Or_Abuse | abuse            | Alcohol           | alcohol         |      0.93 |
+Please check the [FewShot Assertion Classifier](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/2.2.FewShot_Assertion_Classifier.ipynb) Notebook for more information
 
 
-Please check the [Task Based Clinical Pretrained Pipelines](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/11.3.Task_Based_Clinical_Pretrained_Pipelines.ipynb) model for more information
 
 </div><div class="h3-box" markdown="1">
-    
-#### Introducing 2 new Chunk Mapper models designed for medical code mapping between `SNOMED` and `MedDRA` terminologies.
 
-Introducing a suite of new ChunkMapper models designed to streamline medical code mapping tasks. These models include mappings between MedDRA and SNOMED codes, offering a comprehensive solution for interoperability within medical systems.
+#### Introducing a Rule-Based `ContextualAssertion` Annotator to Detect Assertion Status Using Patterns and Rules without any Training or Annotation
+
+Introducing Contextual Assertion which identifies contextual cues within text data, such as negation, uncertainty, etc. It is used for clinical assertion detection, etc. It annotates text chunks with assertions based on configurable rules, prefix and suffix patterns, and exception patterns.
+
+- Dataset: 253 Clinical Texts from in-house dataset
+ 
+|Assertion Label|Contextual Assertion|AssertionDL|
+|---------------|:------------------:|:---------:|
+|Absent         |   0.88             |  0.78  |
+|Past           |   0.77             |  0.65  |
+ 
+
+- Dataset: Used in-house jsl_augmented dataset
+
+|Assertion Label|Contextual Assertion|AssertionDL|
+|---------------|:------------------:|:---------:|
+|Absent         |0.82|0.90|
+|Family         |0.63|0.73|
+|Hypothetical   |0.51|0.69|
+|Past           |0.73|0.77|
+|Planned        |0.57|0.62|
+|Possible       |0.49|0.74|
+|SomeoneElse    |0.61|0.81|
+
+Contextual Assertion, a powerful component within Spark NLP, extends beyond mere negation detection. Its ability to identify and classify a diverse range of contextual cues, including uncertainty, temporality, and sentiment, empowers healthcare professionals to extract deeper meaning from complex medical records.
+
 
-{:.table-model-big}
 | Model Name                                                            |      Description            |
 |-----------------------------------------------------------------------|-----------------------------|
-|[`meddra_llt_snomed_mapper`](https://nlp.johnsnowlabs.com/2024/05/14/meddra_llt_snomed_mapper_en.html)  | This pretrained model maps MedDRA LLT (Lowest Level Term) codes to corresponding SNOMED codes. |
-|[`snomed_meddra_llt_mapper`](https://nlp.johnsnowlabs.com/2024/05/14/snomed_meddra_llt_mapper_en.html)  | This pretrained model maps SNOMED codes to corresponding MedDRA LLT (Lowest Level Term) codes. |
+| [`contextual_assertion_someone_else`](https://nlp.johnsnowlabs.com/2024/06/26/contextual_assertion_someone_else_en.html) |  Identifies contextual cues within text data to detect `someone else` assertions |
+| [`contextual_assertion_absent`](https://nlp.johnsnowlabs.com/2024/07/03/contextual_assertion_absent_en.html) |  Identifies contextual cues within text data to detect `absent` assertions |
+| [`contextual_assertion_past`](https://nlp.johnsnowlabs.com/2024/07/04/contextual_assertion_past_en.html) |  Identifies contextual cues within text data to detect `past` assertions |
 
 *Example*:
 
 ```python
-chunkMapper = ChunkMapperModel.load('meddra_llt_snomed_mapper')\
-    .setInputCols(["meddra_llt_code2chunk"])\
-    .setOutputCol("mappings")\
-    .setRels(["snomed_code"])
-
-text = ["Chronic renal insufficiency", "Gastritis", "Transient ischemic attack"]
+contextual_assertion = ContextualAssertion() \
+    .setInputCols(["sentence", "token", "ner_chunk"]) \
+    .setOutputCol("assertion") \
+    .setPrefixKeywords(["no", "not"]) \
+    .setSuffixKeywords(["unlikely", "negative", "no"]) \
+    .setPrefixRegexPatterns(["\\b(no|without|denies|never|none|free of|not include)\\b"]) \
+    .setSuffixRegexPatterns(["\\b(free of|negative for|absence of|not|rule out)\\b"]) \
+    .setExceptionKeywords(["without"]) \
+    .setExceptionRegexPatterns(["\\b(not clearly)\\b"]) \
+    .addPrefixKeywords(["negative for", "negative"]) \
+    .addSuffixKeywords(["absent", "neither"]) \
+    .setCaseSensitive(False) \
+    .setPrefixAndSuffixMatch(False) \
+    .setAssertion("absent") \
+    .setScopeWindow([2, 2])\
+    .setIncludeChunkToScope(True)\
+
+example_text = """Patient resting in bed. Patient given azithromycin without any difficulty. Patient has audible wheezing, states chest tightness.
+No evidence of hypertension. Patient denies nausea at this time. zofran declined. Patient is also having intermittent sweating associated with pneumonia.
+"""
 ```
 
 *Result*:
 
-{:.table-model-big}
-|                      chunk|meddra_code|                                     snomed_code|
-|---------------------------|-----------|------------------------------------------------|
-|Chronic renal insufficiency|   10050441|723190009:Chronic renal insufficiency (disorder)|
-|                  Gastritis|   10017853|                    4556007:Gastritis (disorder)|
-|  Transient ischemic attack|   10072760|  266257000:Transient ischemic attack (disorder)|
+| ner_chunk      | begin | end  | ner_label | Assertion |
+|----------------|-------|------|-----------|--------|
+| any difficulty | 59    | 72   | PROBLEM   | absent |
+| hypertension   | 149   | 160  | PROBLEM   | absent |
+| nausea         | 178   | 183  | PROBLEM   | absent |
+| zofran         | 199   | 204  | TREATMENT | absent |
 
 
-</div><div class="h3-box" markdown="1">
+Please check the [Contextual Assertion](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/2.3.Contextual_Assertion.ipynb) Notebook for more information
 
 
-####  Improved Version of Social Determinants of Health (SDoH) Named Entity Recognition Model with Reduced Set of Core Entities
 
+</div><div class="h3-box" markdown="1">
+ 
+#### Introducing `VectorDBPostProcessor` Annotator to Filter and Sort the Document Splits Returned by `VectorDB` in a RAG Application
+
+The `VectorDBPostProcessor` is a powerful tool designed to filter and sort output from the `VectorDBModel` (our own VectorDB implementations will be released soon). This processor refines `VECTOR_SIMILARITY_RANKINGS` input annotations and outputs enhanced `VECTOR_SIMILARITY_RANKINGS` annotations based on specified criteria.
 
-We are introducing our new Social Determinants of Health (SDoH) Named Entity Recognition model that has been specifically designed to identify and extract entities related to various social determinants of health. This new model is an improvement over our previous version, aimed at better understanding and tracking the impacts of social factors on health.
+Key Parameters:
+- `filterBy` (str): Select and prioritize filter options (metadata, diversity_by_threshold). Options can be given as a comma-separated string, determining the filtering order. Default: metadata
+- `sortBy` (str): Select sorting option (ascending, descending, lost_in_the_middle, diversity). Default: ascending
+- `caseSensitive` (bool): Determines if string operators' criteria are case-sensitive. Default: False
+- `diversityThreshold` (float): Sets the threshold for the diversity_by_threshold filter. Default: 0.01
+- maxTopKAfterFiltering (int): Limits the number of annotations returned after filtering. Default: 20
+- `allowZeroContentAfterFiltering` (bool): Determines whether zero annotations are allowed after filtering. Default: False
+
+This processor ensures precise and customizable annotation management, making it an essential component for advanced data processing workflows.
 
 *Example*:
 
 ```python
-ner_model = MedicalNerModel.pretrained("ner_sdoh_core", "en", "clinical/models")\
-    .setInputCols(["sentence", "token", "embeddings"])\
-    .setOutputCol("ner")
-
-sample_texts = [["""Smith is 55 years old, living in New York, a divorced Mexcian American woman with financial problems. She speaks Spanish and Portuguese. She lives in an apartment. She has been struggling with diabetes for the past 10 years and has recently been experiencing frequent hospitalizations due to uncontrolled blood sugar levels. Smith works as a cleaning assistant and cannot access health insurance or paid sick leave. She has a son, a student at college. Pt with likely long-standing depression. She is aware she needs rehab. Pt reports having her catholic faith as a means of support as well.  She has a long history of etoh abuse, beginning in her teens. She reports she has been a daily drinker for 30 years, most recently drinking beer daily. She smokes a pack of cigarettes a day. She had DUI in April and was due to court this week."""]]
+post_processor = VectorDBPostProcessor() \
+    .setInputCols("vector_db") \
+    .setOutputCol("post") \
+    .setSortBy("ascending")
+    .setMaxTopKAfterFiltering(5)
+    .setFilterBy("metadata") \
+    .setMetadataCriteria([
+        {"field": "pubdate", "fieldType": "date", "operator": "greater_than", "value": "2017 May 11", "dateFormats": ["yyyy MMM dd", "yyyy MMM d"], "converterFallback": "filter"},
+        {"field": "distance", "fieldType": "float", "operator": "less_than", "value": "0.5470"},
+        {"field": "title", "fieldType": "string", "operator": "contains", "matchMode": "any", "matchValues": ["diabetes", "immune system"]}
+      ])
 ```
 
-*Result*:
+Please check the [VectorDB and PostProcessor for RAG Generative AI](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/45.VectorDB_and_PostProcessor_for_RAG_Generative_AI.ipynb) Notebook for more information
 
-{:.table-model-big}
-|chunk                  |begin|end|label              |
-|-----------------------|-----|---|-------------------|
-|New York               |33   |40 |Geographic_Entity  |
-|financial problems     |82   |99 |Financial_Status   |
-|apartment              |153  |161|Housing            |
-|hospitalizations       |268  |283|Other_SDoH_Keywords|
-|access health insurance|372  |394|Insurance_Status   |
-|son                    |426  |428|Family_Member      |
-|student                |433  |439|Education          |
-|college                |444  |450|Education          |
-|depression             |482  |491|Mental_Health      |
-|rehab                  |517  |521|Access_To_Care     |
-|catholic faith         |546  |559|Spiritual_Beliefs  |
-|support                |575  |581|Social_Support     |
-|daily                  |682  |686|Substance_Frequency|
-|30 years               |700  |707|Substance_Duration |
-|daily                  |738  |742|Substance_Frequency|
-|a pack                 |756  |761|Substance_Quantity |
-|a day                  |777  |781|Substance_Frequency|
-|DUI                    |792  |794|Legal_Issues       |
 
-Please check the [model card](https://nlp.johnsnowlabs.com/2024/04/08/ner_sdoh_core_en.html) and [SDOH Demo](https://demo.johnsnowlabs.com/healthcare/SDOH/)
 
+</div><div class="h3-box" markdown="1">
 
-####  Automating Pipeline Tracing and Analysis with `PipelineTracer` to Help Return Structured JSONs from Pretrained Pipelines Via the `PipelineOuputParser` module
+#### Introducing `ContextSplitAssembler` Annotator to Assemble the Document Post-processed Splits as a Context into an LLM Stage in a RAG Application
 
-`PipelineTracer` is a versatile class designed to trace and analyze the stages of a pipeline, offering in-depth insights into entities, assertions, deidentification, classification, and relationships. It also facilitates the creation of parser dictionaries for building a `PipelineOutputParser`. Key functions include printing the pipeline schema, creating parser dictionaries, and retrieving possible assertions, relations, and entities. Also, provide direct access to parser dictionaries and available pipeline schemas
+The ContextSplitAssembler is a versatile tool designed to work seamlessly with vector databases (our own VectorDB implementations will be released soon) and VectorDBPostProcessor. It combines and organizes annotation results with customizable delimiters and optional splitting.
 
-Please check the [PipelineTracer and PipelineOutputParser](https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/11.4.PipelineTracer_and_PipelineOutputParser.ipynb) notebook for more information
+Key Parameters:
+- `joinString` (str): Specifies the delimiter string inserted between annotations when combining them into a single result. Ensures proper separation and organization. Default: " "
+- `explodeSplits` (bool): Determines whether to split the annotations into separate entries. Default: False
 
+This assembler enhances the management and presentation of annotations, making it an essential tool for advanced data processing workflows.
 
-*PipelineTracer Example*:
+*Example*:
 
 ```python
-from sparknlp.pretrained import PretrainedPipeline
-from sparknlp_jsl.pipeline_tracer import PipelineTracer
+context_split_assembler = ( ContextSplitAssembler()
+  .setInputCols("vector_db")
+  .setOutputCol("document")
+  .setJoinString("\n")
+  .setExplodeSplits(False))
+```
 
-oncology_pipeline = PretrainedPipeline("explain_clinical_doc_oncology", "en", "clinical/models")
+Please check the [VectorDB and PostProcessor for RAG Generative AI](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/45.VectorDB_and_PostProcessor_for_RAG_Generative_AI.ipynb) Notebook for more information
 
-pipeline_tracer = PipelineTracer(oncology_pipeline)
 
-column_maps = pipeline_tracer.createParserDictionary()
-column_maps.update({"document_identifier": "explain_clinical_doc_oncology"})
-print(column_maps)
-```
 
-*column_maps Result*:
+</div><div class="h3-box" markdown="1">
+
+#### SNOMED Entity Resolver Model for `Veterinary` Domains
+
+This advanced model facilitates the mapping of veterinary-related entities and concepts to SNOMED codes using `sbiobert_base_cased_mli` Sentence BERT embeddings. It is trained with an enhanced dataset derived from the `sbiobertresolve_snomed_veterinary_wip` model. The model ensures precise and reliable resolution of veterinary terms to standardized SNOMED codes, aiding in consistent and comprehensive veterinary data documentation and analysis.
+
+*Example*:
 
 ```python
-{
-    'document_identifier': 'explain_clinical_doc_oncology',
-    'document_text': 'document',
-    'entities': [
-        {
-            'ner_chunk_column_name': 'merged_chunk',
-            'assertion_column_name': '',
-            'resolver_column_name': ''
-        },
-        {
-            'ner_chunk_column_name': 'merged_chunk_for_assertion',
-            'assertion_column_name': 'assertion',
-            'resolver_column_name': ''
-        }
-    ],
-    'relations': ['all_relations'],
-    'summaries': [],
-    'deidentifications': [],
-    'classifications': []
- }
+snomed_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_snomed_veterinary", "en", "clinical/models") \
+    .setInputCols(["sentence_embeddings"]) \
+    .setOutputCol("snomed_code")\
+    .setDistanceFunction("EUCLIDEAN")
+text = "The veterinary team is closely monitoring the patient for signs of lymphoblastic lymphoma, a malignant neoplasm of lymphoid origin. They are also treating the patient's osteoarthritis, a degenerative joint disease. Additionally, the team is vigilantly observing the facility for potential outbreaks of mink distemper."
 ```
 
-*PipelineOutputParser Example*:
+*Result*:
 
-```python
-text = """The Patient underwent a computed tomography (CT) scan of the abdomen and pelvis, which showed a complex ovarian mass. A Pap smear performed one month later was positive for atypical glandular cells suspicious for adenocarcinoma. The pathologic specimen showed extension of the tumor throughout the fallopian tubes, appendix, omentum, and 5 out of 5 enlarged lymph nodes. The final pathologic diagnosis of the tumor was stage IIIC papillary serous ovarian adenocarcinoma. Two months later, the patient was diagnosed with lung metastases.Neoadjuvant chemotherapy with the regimens of Cyclophosphamide (500 mg/m2) is being given for 6 cycles with poor response"""
+|                              ner_chunk| entity|    snomed_code|                                         description|
+|---------------------------------------|-------|---------------|----------------------------------------------------|
+|                 lymphoblastic lymphoma|PROBLEM|312281000009102|                              lymphoblastic lymphoma|
+|a malignant neoplasm of lymphoid origin|PROBLEM|      443495005|               neoplasm of lymphoid system structure|
+|           the patient's osteoarthritis|PROBLEM|      201826000|                              erosive osteoarthrosis|
+|           a degenerative joint disease|PROBLEM|      201819000|degenerative joint disease involving multiple joints|
+|                         mink distemper|PROBLEM|348361000009108|                                      mink distemper|
 
-results = oncology_pipeline.fullAnnotate()
+Please check the [model card](https://nlp.johnsnowlabs.com/2024/06/10/sbiobertresolve_snomed_veterinary_en.html) for more information
 
-from sparknlp_jsl.pipeline_output_parser import PipelineOutputParser
 
-pipeline_parser = PipelineOutputParser(column_maps)
-result = pipeline_parser.run(results)
-```
 
-*PipelineOutputParser Result*:
+</div><div class="h3-box" markdown="1">
+    
+#### Voice of the Patients Named Entity Recognition (NER) Model
 
-```python
-{
-    'result': [
-        {
-            'document_identifier': 'explain_clinical_doc_oncology',
-            'document_text': ['The Patient underwent a computed tomography (CT) scan of the abdomen and pelvis, ....'],
-            'entities': [
-                [{'chunk_id': '0',
-                'begin': 24,
-                'end': 42,
-                'chunk': 'computed tomography',
-                'label': 'Imaging_Test',
-                'assertion': None,
-                'term_code': None},
-                {'chunk_id': '1',
-                'begin': 45,
-                'end': 46,
-                'chunk': 'CT',
-                'label': 'Imaging_Test',
-                'assertion': None,
-                'term_code': None},
-                ...
-                ],
-                [{'chunk_id': '0',
-                'begin': 24,
-                'end': 42,
-                'chunk': 'computed tomography',
-                'label': 'Imaging_Test',
-                'assertion': 'Past',
-                'term_code': None},
-                {'chunk_id': '1',
-                'begin': 45,
-                'end': 46,
-                'chunk': 'CT',
-                'label': 'Imaging_Test',
-                'assertion': 'Past',
-                'term_code': None}]
-            ],
-            'relations': [
-                [{'relation': 'is_location_of',
-                'entity1': 'Site_Other_Body_Part',
-                'entity1_begin': '104',
-                'entity1_end': '110',
-                'chunk1': 'ovarian',
-                'entity2': 'Tumor_Finding',
-                'entity2_begin': '112',
-                'entity2_end': '115',
-                'chunk2': 'mass',
-                'confidence': '0.922661'},
-                {'relation': 'is_finding_of',
-                'entity1': 'Pathology_Test',
-                'entity1_begin': '120',
-                'entity1_end': '128',
-                'chunk1': 'Pap smear',
-                'entity2': 'Cancer_Dx',
-                'entity2_begin': '213',
-                'entity2_end': '226',
-                'chunk2': 'adenocarcinoma',
-                'confidence': '0.52542114'},
-                ...]
-            ],
-            'summaries': [],
-            'deidentifications': [],
-            'classifications': []
-        }
-    ]
-}
-```
+The Voice of the Patients NER Model is designed to extract healthcare-related terms from patient-generated documents. This model processes the natural language used by patients to identify and categorize medical terms, facilitating better understanding and documentation of patient-reported information.
 
-*getParserDictDirectly Example*:
+*Example*:
 
 ```python
-from sparknlp_jsl.pipeline_tracer import PipelineTracer
-
-columns_directly = PipelineTracer.getParserDictDirectly("clinical_deidentification", "en", "clinical/models")
+ner_model = MedicalNerModel.pretrained("ner_vop_v2", "en", "clinical/models")\
+    .setInputCols(["sentence", "token", "embeddings"])\
+    .setOutputCol("ner")
 
-print(columns_directly)
+text = "Hello,I'm 20 year old girl. I'm diagnosed with hyperthyroid 1 month ago. I was feeling weak, light headed,poor digestion, panic attacks, depression, left chest pain, increased heart rate, rapidly weight loss, from 4 months. Because of this, I stayed in the hospital and just discharged from hospital. I had many other blood tests, brain mri, ultrasound scan, endoscopy because of some dumb doctors bcs they were not able to diagnose actual problem."
 ```
 
-*getParserDictDirectly Result*:
+*Result*:
+
+|chunk               |begin|end |ner_label             |
+|--------------------|-----|----|----------------------|
+|20 year old         |15   |25  |Age                   |
+|girl                |27   |30  |Gender                |
+|hyperthyroid        |52   |63  |Disease               |
+|1 month ago         |65   |75  |DateTime              |
+|weak                |92   |95  |Symptom               |
+|light               |98   |102 |Symptom               |
+|panic attacks       |127  |139 |PsychologicalCondition|
+|depression          |142  |151 |PsychologicalCondition|
+|left                |154  |157 |Laterality            |
+|chest               |159  |163 |BodyPart              |
+|pain                |165  |168 |Symptom               |
+|increased           |171  |179 |TestResult            |
+|heart rate          |181  |190 |VitalTest             |
+|rapidly             |193  |199 |Modifier              |
+|weight loss         |201  |211 |Symptom               |
+|4 months            |220  |227 |Duration              |
+|hospital            |263  |270 |ClinicalDept          |
+|discharged          |281  |290 |AdmissionDischarge    |
+|hospital            |297  |304 |ClinicalDept          |
+|blood tests         |324  |334 |Test                  |
+|brain               |337  |341 |BodyPart              |
+|mri                 |343  |345 |Test                  |
+|ultrasound scan     |348  |362 |Test                  |
+|endoscopy           |365  |373 |Procedure             |
+
+Please check the [model card](https://nlp.johnsnowlabs.com/2024/06/07/ner_vop_v2_en.html) for more information
 
-```python
-{
-    'document_identifier': 'clinical_deidentification',
-    'document_text': 'sentence',
-    'entities': [{
-        'ner_chunk_column_name': 'ner_chunk',
-        'assertion_column_name': '',
-        'resolver_column_name': ''}],
-    'relations': [],
-    'summaries': [],
-    'deidentifications': [{
-        'original': 'sentence',
-        'obfuscated': 'obfuscated',
-        'masked': ''}],
-    'classifications': []}
 
-```
 
 </div><div class="h3-box" markdown="1">
 
-####  Configuring Age-based Obfuscation with the `setAgeGroups` Parameter
+#### New Rule-Based Entity Matcher Models to Customise De-IDentification Pipelines
 
-This method, setAgeGroups, is used in conjunction with the `obfuscateByAgeGroups` parameter to specify age ranges for obfuscation. If the specified `ageGroups` dictionary does not cover all ages, the obfuscation defaults to the `ageRanges` parameter. Each entry in the dictionary includes an age group name paired with a range of two integers: the lower and upper bounds of the age group. By default, the method includes a standard dictionary of age groups in English, but users can customize this dictionary to suit specific age classifications and obfuscation requirements. This method takes a value parameter containing a dictionary mapping age group names to corresponding age ranges for obfuscation.
+We introduce a suite of text and regex matchers, specifically designed to enhance the deidentification and clinical document understanding process with rule-based methods. 
 
+| Model Name                               |      Description            |
+|------------------------------------------|-----------------------------|
+| [`cancer_diagnosis_matcher`](https://nlp.johnsnowlabs.com/2024/06/17/cancer_diagnosis_matcher_en.html) | This model extracts cancer diagnoses in clinical notes using a rule-based `TextMatcherInternal` annotator. |
+| [`country_matcher`](https://nlp.johnsnowlabs.com/2024/06/19/country_matcher_en.html) | This model extracts countries in clinical notes using a rule-based `TextMatcherInternal` annotator. |
+| [`email_matcher`](https://nlp.johnsnowlabs.com/2024/06/19/email_matcher_en.html) | This model extracts emails in clinical notes using a rule-based `RegexMatcherInternal` annotator. |
+| [`phone_matcher`](https://nlp.johnsnowlabs.com/2024/06/19/phone_matcher_en.html) | This model extracts phone entities in clinical notes using a rule-based `RegexMatcherInternal` annotator. |
+| [`state_matcher`](https://nlp.johnsnowlabs.com/2024/06/19/state_matcher_en.html) | This model extracts states in clinical notes using a rule-based `RegexMatcherInternal` annotator. |
+| [`zip_matcher`](https://nlp.johnsnowlabs.com/2024/06/19/zip_matcher_en.html) | This model extracts zip codes in clinical notes using a rule-based `RegexMatcherInternal` annotator. |
+| [`city_matcher`](https://nlp.johnsnowlabs.com/2024/07/02/city_matcher_en.html) | This model extracts city names in clinical notes using a rule-based `TextMatcherInternal` annotator. |
 
 *Example*:
 
 ```python
-obfuscation = DeIdentification()\
-    .setInputCols(["sentence", "token", "ner_subentity_chunk"]) \
-    .setOutputCol("deidentified") \
-    .setMode("obfuscate")\
-    .setObfuscateByAgeGroups(True)\
-    .setAgeGroups({"baby": [0, 1],
-                   "toddler": [1, 4],
-                   "child": [4, 13],
-                   "teenager": [13, 20],
-                   "adult": [20, 65],
-                   "senior": [65, 100] })
-
-text ='''
-Name: Joseph Brown, Age: 17, Phone: (9) 7765-5632.
-This 17 yrs old male, presented with chest heaviness that started during a pick-up basketball game. 
-Mark Smith, aged 55, and his daughter (7) Mary were involved in an accident during their travel.
-'''
+text_matcher = TextMatcherInternalModel.pretrained("cancer_diagnosis_matcher", "en", "clinical/models") \
+    .setInputCols(["sentence", "token"])\
+    .setOutputCol("cancer_dx")\
+    .setMergeOverlapping(True)
+
+example_text = """A 65-year-old woman had a history of debulking surgery, bilateral oophorectomy with omentectomy, total anterior hysterectomy with radical pelvic lymph nodes dissection due to ovarian carcinoma (mucinous-type carcinoma, stage Ic) 1 year ago. The patient's medical compliance was poor and failed to complete her chemotherapy (cyclophosphamide 750 mg/m2, carboplatin 300 mg/m2). Recently, she noted a palpable right breast mass, 15 cm in size which nearly occupied the whole right breast in 2 months. Core needle biopsy revealed metaplastic carcinoma. Neoadjuvant chemotherapy with the regimens of Taxotere (75 mg/m2), Epirubicin (75 mg/m2), and Cyclophosphamide (500 mg/m2) was given for 6 cycles with poor response, followed by a modified radical mastectomy (MRM) with dissection of axillary lymph nodes and skin grafting. Postoperatively, radiotherapy was done with 5000 cGy in 25 fractions. The histopathologic examination revealed a metaplastic carcinoma with squamous differentiation associated with adenomyoepithelioma. Immunohistochemistry study showed that the tumor cells are positive for epithelial markers-cytokeratin (AE1/AE3) stain, and myoepithelial markers, including cytokeratin 5/6 (CK 5/6), p63, and S100 stains.  Expressions of hormone receptors, including ER, PR, and Her-2/Neu, were all negative."""
 ```
 
 *Result*:
 
-{:.table-model-big}
-|sentence|deidentified|
-|--------|------------|
-|Name: Joseph Brown, Age: 17, Phone: (9) 7765-5632.                                                 |Name: Burnadette Carrion, Age: teenager, Phone: (6) 9846-1747.                                     |
-|This 17 yrs old male, presented with chest heaviness that started during a pick-up basketball game.|This teenager male, presented with chest heaviness that started during a pick-up basketball game. |
-|Mark Smith, aged 55, and his daughter (7) Mary were involved in an accident during their travel.   |Cleve Dale, adult, and his daughter (child) Mary were involved in an accident during their travel.|
+|                  chunk|begin| end|    label|
+|-----------------------|-----|----|---------|
+|      ovarian carcinoma|  176| 192|Cancer_dx|
+|mucinous-type carcinoma|  195| 217|Cancer_dx|
+|  metaplastic carcinoma|  528| 548|Cancer_dx|
+|  metaplastic carcinoma|  937| 957|Cancer_dx|
+|    adenomyoepithelioma| 1005|1023|Cancer_dx|
+
 
 
 </div><div class="h3-box" markdown="1">
+ 
+#### New NER, Assertion, Relation Extraction, and Classification Models to Identify `Alcohol` and `Smoking` Related Medical Entities
+
+A suite of models designed for the identification and analysis of alcohol and smoking related entities in text data. These models include Named Entity Recognition (NER), assertion status, relation extraction, and classification, providing a comprehensive toolkit for analyzing substance use information.
 
+- NER Model
 
-#### Enhancing Date Obfuscation Control with the `setKeepYear` Parameter in `Deidentification` Annotator to Allow `year` Info Intact
+|Model Name         | Predicted Entities | Description      |
+|-------------------|--------------------|------------------|
+| [ner_alcohol_smoking](https://nlp.johnsnowlabs.com/2024/07/01/ner_alcohol_smoking_en.html) | `Drinking_Status`, `Alcohol_Type`, `Smoking_Status`, `Smoking_Type`, `Substance_Duration`, `Substance_Frequency`, `Substance_Quantity`, `Cardiovascular_Issues`, `Respiratory_Issues`, `GUT_Issues`, `Neurologic_Issues`, `Psychiatric_Issues`, `Other_Health_Issues`, `Drinking_Environment`, `Cessation_Treatment`, `Withdrawal_Treatment` | Detects alcohol and smoking related entities within text data |
 
-The `setKeepYear` parameter to improve date obfuscation controls. This feature allows users to decide whether to retain the year in date entities while obfuscating the month and day. The default setting is False.
+This `ner_alcohol_smoking` model is designed to detect and label alcohol and smoking-related entities within text data. Alcohol refers to beverages containing ethanol, a psychoactive substance that is widely consumed for its pleasurable effects. Smoking typically involves inhaling smoke from burning tobacco, a highly addictive substance. The model has been trained using advanced deep learning techniques on a diverse range of text sources and can accurately recognize and classify a wide range of entities related to alcohol and smoking. The model’s accuracy and precision have been carefully validated against expert-labeled data to ensure reliable and consistent results.
 
-- `True`, the year remains unchanged, ensuring consistency in data that relies on year-specific information.
-- `False`, the entire date, including the year, will be modified. 
 
 *Example*:
 
 ```python
-obfuscation = DeIdentification()\
-    .setInputCols(["sentence", "token", "ner_subentity_chunk"]) \
-    .setOutputCol("deidentified") \
-    .setMode("obfuscate")\
-    .setObfuscateDate(True)\
-    .setObfuscateRefSource("faker") \
-    .setKeepYear(True)
-```
 
-*.setKeepYear(False) Result*:
+ner_model = MedicalNerModel.pretrained("ner_alcohol_smoking", "en", "clinical/models")\
+    .setInputCols(["sentence", "token","embeddings"])\
+    .setOutputCol("ner")
 
-{:.table-model-big}
-|sentence                                                 |deidentified                                             |
-|---------------------------------------------------------|---------------------------------------------------------|
-|Hendrickson, Ora, Record date: 2023-01-01, Age: 25 .     |Delle Ferdinand, Record date: 2023-02-10, Age: 35 .      |
-|He was admitted to hospital for cystectomy on 12/31/2022.|He was admitted to hospital for cystectomy on 02/09/2023.|
+sample_texts = ["""The outpatient clinic addressed a complaint from the patient regarding severe anxiety and withdrawal symptoms. 
+He disclosed a history of alcohol addiction, with weekly episodes of intense binge drinking over the past decade. 
+However, due to recent challenges in his personal and professional life, he decided to quit drinking cold turkey a week ago. 
+Since then, he has been experiencing escalating symptoms including tremors, sweating, nausea, and severe anxiety. 
+The patient denies recent use drugs or smoking, focusing her struggles solely on alcohol.
+He was placed on CIWA protocol w/ lorazepam for management. Scheduled for cognitive-behavioral therapy (CBT)."""]
+```
 
-*.setKeepYear(True) Result*:
+*Result*:
 
-{:.table-model-big}
-|sentence                                                 |deidentified                                             |
-|---------------------------------------------------------|---------------------------------------------------------|
-|Hendrickson, Ora, Record date: 2023-01-01, Age: 25 .     |Lenord Radon, Record date: 2023-02-07, Age: 31 .         |
-|He was admitted to hospital for cystectomy on 12/31/2022.|He was admitted to hospital for cystectomy on 02/06/2022.|
+|chunk                       |begin|end|ner_label             |
+|----------------------------|-----|---|----------------------|
+|anxiety                     |78   |84 |Psychoneurologic_Issue|
+|alcohol addiction           |138  |154|Drinking_Status       |
+|weekly                      |162  |167|Substance_Frequency   |
+|binge                       |189  |193|Substance_Quantity    |
+|drinking                    |195  |202|Drinking_Status       |
+|over the past decade        |204  |223|Substance_Duration    |
+|drinking                    |319  |326|Drinking_Status       |
+|tremors                     |420  |426|Psychoneurologic_Issue|
+|sweating                    |429  |436|Other_Health_Issues   |
+|nausea                      |439  |444|GUT_Issues            |
+|anxiety                     |458  |464|Psychoneurologic_Issue|
+|smoking                     |507  |513|Smoking_Status        |
+|alcohol                     |549  |555|Drinking_Status       |
+|CIWA                        |575  |578|Withdrawal_Treatment  |
+|lorazepam                   |592  |600|Withdrawal_Treatment  |
+|cognitive-behavioral therapy|632  |659|Cessation_Treatment   |
+|CBT                         |662  |664|Cessation_Treatment   |
+
+- Assertion Models
+
+|Model Name         | Assertion Status | Description      |
+|-------------------|-------------------|------------------|
+| [assertion_alcohol_smoking_wip](https://nlp.johnsnowlabs.com/2024/07/03/assertion_alcohol_smoking_wip_en.html) | `Absent`, `Hypothetical_Possible`, `Past_History`, `Present_Planned` | This model detects the assertion status of entities related to alcohol-smoking. |
+| [assertion_alcohol_smoking_general_symptoms_wip](https://nlp.johnsnowlabs.com/2024/07/03/assertion_alcohol_smoking_general_symptoms_wip_en.html) | `Overdose_Symptom`, `Withdrawal_Symptom` | This model detects the assertion status of general symptoms entity related to alcohol-smoking. |
 
-As you can see, `2022` has not been changed.
+*Example*:
 
+```python
+assertion = AssertionDLModel.pretrained("assertion_alcohol_smoking_wip", "en", "clinical/models")\
+    .setInputCols(["sentence", "ner_chunk", "embeddings"]) \
+    .setOutputCol("assertion")
 
-####  Broadening Relation Extraction with Extended Scope-Windows, `directionSensitive` and `filterByTokenDistance` Parameters to Allow Further Customization and Reduce FPs
+sample_texts = ["""Per the patient, the last drink was on ___, prior to admission. The patient admits to having experienced tremors, palpitations, and diaphoresis during the past alcohol withdrawals, but he denies ever having experienced seizures. Mr. ___ did not report experiencing any symptoms of withdrawal throughout his hospital stay, and an examination revealed no evidence of withdrawal.""",
+               """SUBSTANCE ABUSE: The patient admitted to occasional binge drinking, but admitted to normally consuming one pint of liquor a day in the week before her admission. Before she attempted suicide, she was heavily intoxicated and had a high blood alcohol level (BAL). Attending the AA meetings and expressing a desire to keep going to AA to support sobriety were two ways the patient showed motivation to stop drinking. The patient was put on the CIWA protocol upon admission, but no PRN Valium was needed for alcohol withdrawal."""]
+```
 
--  In RelationRxtraction, scopeWindow expands beyond the immediate tokens of target chunks. By applying a `scopeWindow [X, Y]`, additional `X` tokens to the left and `Y` tokens to the right become crucial for feature generation, enriching contextual information essential for precise embeddings-based feature extraction.
+*Result*:
+
+|chunk       |begin|end|ner_label             |assertion      |confidence|
+|------------|-----|---|----------------------|---------------|----------|
+|drink       |26   |30 |Drinking_Status       |Past_History   |0.8507    |
+|tremors     |105  |111|Psychoneurologic_Issue|Past_History   |0.9315    |
+|palpitations|114  |125|Cardiovascular_Issues |Past_History   |0.9251    |
+|diaphoresis |132  |142|Other_Health_Issues   |Past_History   |0.9181    |
+|alcohol     |160  |166|Drinking_Status       |Past_History   |0.9109    |
+|seizures    |219  |226|Psychoneurologic_Issue|Absent         |0.5359    |
+|binge       |52   |56 |Substance_Quantity    |Present_Planned|0.5528    |
+|drinking    |58   |65 |Drinking_Status       |Present_Planned|0.5704    |
+|one pint    |103  |110|Substance_Quantity    |Present_Planned|0.6838    |
+|liquor      |115  |120|Alcohol_Type          |Present_Planned|0.6879    |
+|a day       |122  |126|Substance_Frequency   |Present_Planned|0.8029    |
+|suicide     |183  |189|Psychoneurologic_Issue|Past_History   |0.731     |
+|intoxicated |208  |218|Psychoneurologic_Issue|Past_History   |0.7832    |
+|alcohol     |241  |247|Drinking_Status       |Past_History   |0.507     |
+|AA          |276  |277|Cessation_Treatment   |Present_Planned|0.4559    |
+|AA          |329  |330|Cessation_Treatment   |Present_Planned|0.5112    |
+|drinking    |404  |411|Drinking_Status       |Present_Planned|0.5385    |
+|CIWA        |441  |444|Withdrawal_Treatment  |Present_Planned|0.5693    |
+|Valium      |482  |487|Withdrawal_Treatment  |Absent         |0.553     |
+|alcohol     |504  |510|Drinking_Status       |Present_Planned|0.5135    |
+
+- Relation Extraction Model
+
+|Model Name         | Predicted Entities | Description      |
+|-------------------|--------------------|------------------|
+| [re_alcohol_smoking_clinical_wip](https://nlp.johnsnowlabs.com/2024/07/03/re_alcohol_smoking_clinical_wip_en.html) | `is_caused_by`, `is_used_for` | It recognizes relations between treatment cessation and withdrawal with drinking and smoking status, as well as relations between various health issues (Neurologic, Psychiatric, Cardiovascular, Respiratory, GUT, and Other Health Issues) and drinking and smoking status. |
 
 *Example*:
 
 ```python
-re_model = RelationExtractionModel.pretrained("re_oncology_wip", "en", "clinical/models") \
-    .setInputCols(["embeddings", "pos_tags", "ner_chunk", "dependencies"]) \
-    .setOutputCol("re_oncology_results") \
-    .setScopeWindow([5,5])
+clinical_re_Model = RelationExtractionModel()\
+    .pretrained("re_alcohol_smoking_clinical_wip", "en", "clinical/models")\
+    .setInputCols(["embeddings", "pos_tags", "ner_chunk", "dependencies"])\
+    .setOutputCol("relations")\
+    .setMaxSyntacticDistance(4)\
+    .setRelationPairs(["Cessation_Treatment-Drinking_Status",
+    "Cessation_Treatment-Smoking_Status",
+    "Respiratory_Issues-Drinking_Status",
+    "Respiratory_Issues-Smoking_Status"])
+
+text = ["""Pulmonary Function Tests: Demonstrates airflow limitation consistent with chronic obstructive pulmonary disease
+ (COPD). Diagnosis: Acute exacerbation of COPD secondary to smoking.
+ Diagnosis: Alcoholic fatty liver disease and smoking-related respiratory symptoms.Management: The patient received alcohol cessation counseling and support services to address her alcohol use disorder. She was also provided with smoking cessation pharmacotherapy and behavioral interventions to help her quit smoking."""]
 ```
 
-- The features `directionSensitive` and `filterByTokenDistance` have been implemented. The `directionSensitive` setting determines how entity relations are considered. If set to true, only relations in the form of ENTITY1-ENTITY2 are considered. If set to false, both ENTITY1-ENTITY2 and ENTITY2-ENTITY1 relations are considered. The `filterByTokenDistance` setting is a criterion for filtering based on the number of tokens between entities. The model only identifies relations where the entities are separated by fewer than the specified number of tokens.
+*Result*:
 
-*directionSensitive Example*:
+|    |   sentence |   entity1_begin |   entity1_end | chunk1   | entity1            |   entity2_begin |   entity2_end | chunk2                    | entity2             | relation     |   confidence |
+|---:|-----------:|----------------:|--------------:|:---------|:-------------------|----------------:|--------------:|:--------------------------|:--------------------|:-------------|-------------:|
+|  0 |          2 |             154 |           157 | COPD     | Respiratory_Issues |             172 |           178 | smoking                   | Smoking_Status      | is_caused_by |     0.999902 |
+|  2 |          4 |             297 |           303 | alcohol  | Drinking_Status    |             305 |           324 | cessation counseling      | Cessation_Treatment | is_used_for  |     0.999512 |
+|  3 |          4 |             297 |           303 | alcohol  | Drinking_Status    |             330 |           345 | support services          | Cessation_Treatment | is_used_for  |     0.933377 |
+|  4 |          5 |             411 |           417 | smoking  | Smoking_Status     |             419 |           443 | cessation pharmacotherapy | Cessation_Treatment | is_used_for  |     0.996433 |
+|  5 |          5 |             411 |           417 | smoking  | Smoking_Status     |             449 |           472 | behavioral interventions  | Cessation_Treatment | is_used_for  |     0.9565   |
 
-```python
-re_ner_chunk_filter = RENerChunksFilter() \
-    .setInputCols(["ner_chunk", "dependencies"])\
-    .setOutputCol("re_ner_chunk")\
-    .setMaxSyntacticDistance(10)\
-    .setDirectionSensitive(True)\
-    .setRelationPairs(["test-problem", #"problem-test"
-                       "treatment-problem", #"problem-treatment"
-                       ])\
-
-redl_model = RelationExtractionDLModel.pretrained("redl_clinical_biobert", "en", "clinical/models")\
-    .setInputCols(["re_ner_chunk", "sentence"])\
-    .setOutputCol("relations")\
-    .setPredictionThreshold(0.5)\
-    .setRelationPairsCaseSensitive(False)\
+- Classification Models
+
+|Model Name         | Assertion Status | Description      |
+|-------------------|-------------------|------------------|
+| [genericclassifier_alcohol_mpnet_wip](https://nlp.johnsnowlabs.com/2024/07/01/genericclassifier_alcohol_mpnet_wip_en.html) | `Current_Drinker`, `Others` | The primary goal of the model is to categorize texts into two main label categories: 'Current_Drinker' and 'Others.' (past or non-smoker) |
+| [genericclassifier_smoking_mpnet_wip](https://nlp.johnsnowlabs.com/2024/07/01/genericclassifier_smoking_mpnet_wip_en.html) | `Current_Smoker`, `Others` | The primary goal of the model is to categorize texts into two main label categories: 'Current_Smoker' and 'Others.' (past or non-smoker) |
 
+*Example*:
 
-text = ''' She was treated with a five-day course of amoxicillin for a respiratory tract infection.
-She was on metformin, glipizide, and dapagliflozin for T2DM and additionally atorvastatin and gemfibrozil for HTG.
-However, serum chemistry obtained six hours after presentation revealed the anion gap was still elevated at 21, serum bicarbonate was 16 mmol/L, and lipase was 52 U/L.
-The β-hydroxybutyrate level was found to be elevated at 5.29 mmol/L - the original sample was centrifuged and the chylomicron layer was removed before analysis due to interference from turbidity caused by lipemia again.
-'''
+```python
+generic_classifier = GenericClassifierModel.pretrained('genericclassifier_alcohol_mpnet_wip', 'en', 'clinical/models')\
+    .setInputCols("features")\
+    .setOutputCol("prediction")
+
+text_list = [
+             "The patient, with a history of COPD and alcohol dependence, was initially admitted due to a COPD exacerbation and community-acquired pneumonia. The situation was further complicated by alcohol withdrawal. He was later transferred to another facility for treatment of left hand cellulitis, which raised concerns for necrotizing fasciitis.",
+             "Until recently, the patient had maintained stability on his antidepressant regimen. However, he experienced a notable worsening of depressive symptoms last week, leading him to engage in heavy binge drinking as an ineffective way to suppress his emotional distress and feelings of despair.",
+             "Ms. Jane Doe, a 60-year-old retired teacher, presented to the emergency department complaining of severe abdominal pain and vomiting. She has a history of gallstones but has been asymptomatic for years. Currently, she does not smoke or drink alcohol, focusing on a healthy lifestyle.",
+             "Mr. John Smith, a 45-year-old accountant, came to the clinic reporting intense chest pain and shortness of breath. He has a history of hypertension but has managed it well with medication. He currently does not smoke or drink alcohol, maintaining a healthy lifestyle."]
 ```
 
-*directionSensitive Result*:
-
-{:.table-model-big.db}
-|sentence |entity1_begin|entity1_end| chunk1                      | entity1   |entity2_begin |entity2_end | chunk2                        | entity2| relation|confidence |
-|--------:|-------------|-----------|:----------------------------|:----------|-------------:|-----------:|:------------------------------|:-------|:--------|----------:|
-|       0 |           43|         53| amoxicillin                 | TREATMENT |           59 |         87 | a respiratory tract infection | PROBLEM| Treatment_Administered_Problem |  0.998835 |
-|       1 |          101|        109| metformin                   | TREATMENT |          145 |        148 | T2DM                          | PROBLEM| Treatment_Administered_Problem |  0.995263 |
-|       1 |          101|        109| metformin                   | TREATMENT |          200 |        202 | HTG                           | PROBLEM| Treatment_Administered_Problem |  0.749655 |
-|       1 |          112|        120| glipizide                   | TREATMENT |          145 |        148 | T2DM                          | PROBLEM| Treatment_Administered_Problem |  0.993901 |
-|       1 |          112|        120| glipizide                   | TREATMENT |          200 |        202 | HTG                           | PROBLEM| Treatment_Administered_Problem |  0.839519 |
-|       1 |          127|        139| dapagliflozin               | TREATMENT |          145 |        148 | T2DM                          | PROBLEM| Treatment_Administered_Problem |  0.99619  |
-|       1 |          127|        139| dapagliflozin               | TREATMENT |          200 |        202 | HTG                           | PROBLEM| Treatment_Administered_Problem |  0.984917 |
-|       1 |          167|        178| atorvastatin                | TREATMENT |          200 |        202 | HTG                           | PROBLEM| Treatment_Administered_Problem |  0.935767 |
-|       1 |          184|        194| gemfibrozil                 | TREATMENT |          200 |        202 | HTG                           | PROBLEM| Treatment_Administered_Problem |  0.983878 |
-|       2 |          214|        228| serum chemistry             | TEST      |          295 |        308 | still elevated                | PROBLEM| Test_Revealed_Problem      |  0.997158 |
-|       2 |          277|        289| the anion gap               | TEST      |          295 |        308 | still elevated                | PROBLEM| Test_Revealed_Problem      |  0.989831 |
-|       3 |          373|        399| The β-hydroxybutyrate level | TEST      |          417 |        424 | elevated                      | PROBLEM| Test_Revealed_Problem      |  0.996874 |
-|       3 |          373|        399| The β-hydroxybutyrate level | TEST      |          540 |        551 | interference                  | PROBLEM| Test_Revealed_Problem      |  0.964988 |
-|       3 |          373|        399| The β-hydroxybutyrate level | TEST      |          558 |        566 | turbidity                     | PROBLEM| Test_Revealed_Problem      |  0.972585 |
-|       3 |          373|        399| The β-hydroxybutyrate level | TEST      |          578 |        584 | lipemia                       | PROBLEM| Test_Revealed_Problem      |  0.976935 |
-|       3 |          524|        531| analysis                    | TEST      |          558 |        566 | turbidity                     | PROBLEM| Test_Performed_Problem     |  0.537359 |
-|       3 |          524|        531| analysis                    | TEST      |          578 |        584 | lipemia                       | PROBLEM| Test_Performed_Problem     |  0.850083 |
-
-
-
-*filterByTokenDistance Example*:
+*Result*:
 
-```python
-re_ner_chunk_filter = RENerChunksFilter() \
-    .setInputCols(["ner_chunk", "dependencies"])\
-    .setOutputCol("re_ner_chunk")\
-    .setMaxSyntacticDistance(10)\
-    .setDirectionSensitive(True)\
-    .setRelationPairs(["test-problem", #"problem-test"
-                       "treatment-problem", #"problem-treatment"
-                       ])\
-    .setFilterByTokenDistance(4)
-
-redl_model = RelationExtractionDLModel.pretrained("redl_clinical_biobert", "en", "clinical/models")\
-    .setInputCols(["re_ner_chunk", "sentence"])\
-    .setOutputCol("relations")\
-    .setPredictionThreshold(0.5)\
-    .setRelationPairsCaseSensitive(False)\
+|                                                                                                text|           result|
+|----------------------------------------------------------------------------------------------------|-----------------|
+|The patient, with a history of COPD and alcohol dependence, was initially admitted due to a COPD ...|Current_Drinker|
+|Until recently, the patient had maintained stability on his antidepressant regimen. However, he e...|Current_Drinker|
+|Ms. Jane Doe, a 60-year-old retired teacher, presented to the emergency department complaining of...|         Others|
+|Mr. John Smith, a 45-year-old accountant, came to the clinic reporting intense chest pain and sho...|         Others|
 
-text = ''' She was treated with a five-day course of amoxicillin for a respiratory tract infection.
-She was on metformin, glipizide, and dapagliflozin for T2DM and additionally atorvastatin and gemfibrozil for HTG.
-However, serum chemistry obtained six hours after presentation revealed the anion gap was still elevated at 21, serum bicarbonate was 16 mmol/L, and lipase was 52 U/L.
-The β-hydroxybutyrate level was found to be elevated at 5.29 mmol/L - the original sample was centrifuged and the chylomicron layer was removed before analysis due to interference from turbidity caused by lipemia again.
-'''
 
-```
+</div><div class="h3-box" markdown="1">
+ 
+#### New NER and Assertion Models to Extract `Menopause` Related Entities
 
-*filterByTokenDistance Result*:
+A set of sophisticated models aimed at extracting and analyzing menopause-related entities in text data. These models include a Named Entity Recognition (NER) model and assertion models, which identify and determine the status of various menopause-related terms, aiding in comprehensive menopause data analysis.
 
-{:.table-model-big.db}
-|sentence|entity1_begin |entity1_end | chunk1                      | entity1   |entity2_begin |entity2_end | chunk2                        | entity2| relation|confidence|
-|-------:|-------------:|-----------:|:----------------------------|:----------|-------------:|-----------:|:------------------------------|:-------|:--------|------:|
-|       0|           43 |         53 | amoxicillin                 | TREATMENT |           59 |         87 | a respiratory tract infection | PROBLEM| Treatment_Administered_Problem    |  0.99 |
-|       1|          101 |        109 | metformin                   | TREATMENT |          145 |        148 | T2DM                          | PROBLEM| Treatment_Administered_Problem    |  0.99 |
-|       1|          112 |        120 | glipizide                   | TREATMENT |          145 |        148 | T2DM                          | PROBLEM| Treatment_Administered_Problem    |  0.99 |
-|       1|          127 |        139 | dapagliflozin               | TREATMENT |          145 |        148 | T2DM                          | PROBLEM| Treatment_Administered_Problem    |  0.99 |
-|       1|          167 |        178 | atorvastatin                | TREATMENT |          200 |        202 | HTG                           | PROBLEM| Treatment_Administered_Problem    |  0.94 |
-|       1|          184 |        194 | gemfibrozil                 | TREATMENT |          200 |        202 | HTG                           | PROBLEM| Treatment_Administered_Problem    |  0.98 |
-|       2|          277 |        289 | the anion gap               | TEST      |          295 |        308 | still elevated                | PROBLEM| Test_Revealed_Problem    |  0.98 |
-|       3|          373 |        399 | The β-hydroxybutyrate level | TEST      |          417 |        424 | elevated                      | PROBLEM| Test_Revealed_Problem    |  0.99 |
-|       3|          524 |        531 | analysis                    | TEST      |          558 |        566 | turbidity                     | PROBLEM| Test_Performed_Problem   |  0.54 |
-|       3|          524 |        531 | analysis                    | TEST      |          578 |        584 | lipemia                       | PROBLEM| Test_Performed_Problem   |  0.85 |
+- NER Model
 
+|Model Name         | Predicted Entities | Description      |
+|-------------------|--------------------|------------------|
+| [ner_menopause_core](https://nlp.johnsnowlabs.com/2024/07/03/ner_menopause_core_en.html) | `Perimenopause`, `Menopause`, `Gynecological_Symptom`, `Gynecological_Disease`, `Other_Symptom`, `Irregular_Menstruation`, `G_P`, `Hypertension`, `Osteoporosis`, `Oncological`, `Fracture`, `Hormone_Replacement_Therapy`, `Osteporosis_Therapy`, `Antidepressants`, `Procedure`, `Hormone_Testing`, `Vaginal_Swab`, `Age`, `Test_Result` | Detects menopause related entities within text data |
 
-please see the blogpost [Next-Level Relation Extraction in Healthcare NLP: Introducing New Directional and Contextual Features](https://medium.com/john-snow-labs/next-level-relation-extraction-in-healthcare-nlp-introducing-new-directional-and-contextual-cc54eb68a699)
+This `ner_menopause_core` model is designed to detect and label core entities related to menopause and associated conditions within text data. Menopause-related terms and conditions are crucial factors that influence individuals’ health outcomes, especially among women undergoing the menopausal transition. The model has been trained using advanced machine-learning techniques on a diverse range of text sources. It can accurately recognize and classify a wide range of menopause-related entities. The model’s accuracy and precision have been carefully validated against expert-labeled data to ensure reliable and consistent results.
 
 
-</div><div class="h3-box" markdown="1">
+*Example*:
 
-#### Enhancing Rule-Based Annotators with the `ner_source` Field for Improved Chunk Tracking and Prioritization
+```python
 
-Enhancing rule-based annotators such as `ContextualParser`, `TextMatcherInternal`, `RegexMatcherInternal`, and `EntityRulerInternal` with `ner_source` field for improved chunk tracking and prioritization
+ner_model = MedicalNerModel.pretrained("ner_menopause_core", "en", "clinical/models"))\
+    .setInputCols(["sentence", "token","embeddings"])\
+    .setOutputCol("ner")
 
-We have enhanced rule-based annotators, including `ContextualParser`, `TextMatcherInternal`, `RegexMatcherInternal`, and `EntityRulerInternal`, by adding the `ner_source` field. This improvement allows for better chunk tracking and prioritization, enabling clients to trace the origin of chunks effectively. Additionally, with the `ner_source` field, `NerConverterInternal` and `ChunkMergerApproach` can now prioritize chunks using the `.setChunkPrecedence("ner_source")` method, leading to more accurate and efficient entity recognition and handling.
+sample_texts = ["""The patient is a 52-year-old female, G3P2, who presents with complaints of irregular menstruation and symptoms suggestive of perimenopause. She reports experiencing hot flashes, night sweats, and vaginal dryness. Her medical history includes polycystic ovary syndrome (PCOS), fatigue, mood swings, hypertension diagnosed 5 years ago and currently managed with medication, and osteoporosis diagnosed 2 years ago with ongoing treatment. 
+Current medications include estradiol for hormone replacement therapy, alendronate for osteoporosis therapy, and fluoxetine for depressive symptoms related to menopause. Recent tests and procedures include a bone density scan to monitor osteoporosis, blood tests for estradiol and follicle-stimulating hormone (FSH) levels, and a vaginal swab collected for routine infection screening. Test results showed elevated FSH levels indicating menopause."""]
+```
+
+*Result*:
+
+|chunk                       |begin|end |ner_label                  |
+|----------------------------|-----|----|---------------------------|
+|irregular menstruation      |76   |97  |Irregular_Menstruation     |
+|perimenopause               |126  |138 |Perimenopause              |
+|hot flashes                 |166  |176 |Other_Symptom              |
+|night sweats                |179  |190 |Other_Symptom              |
+|vaginal dryness             |197  |211 |Gynecological_Symptom      |
+|polycystic ovary syndrome   |243  |267 |Gynecological_Disease      |
+|PCOS                        |270  |273 |Gynecological_Disease      |
+|fatigue                     |277  |283 |Other_Symptom              |
+|hypertension                |299  |310 |Hypertension               |
+|osteoporosis                |377  |388 |Osteoporosis               |
+|estradiol                   |466  |474 |Hormone_Replacement_Therapy|
+|hormone replacement therapy |480  |506 |Hormone_Replacement_Therapy|
+|alendronate                 |509  |519 |Osteporosis_Therapy        |
+|osteoporosis                |525  |536 |Osteoporosis               |
+|fluoxetine                  |551  |560 |Antidepressants            |
+|menopause                   |597  |605 |Menopause                  |
+|osteoporosis                |675  |686 |Osteoporosis               |
+|estradiol                   |705  |713 |Hormone_Testing            |
+|follicle-stimulating hormone|719  |746 |Hormone_Testing            |
+|FSH                         |749  |751 |Hormone_Testing            |
+|vaginal swab                |768  |779 |Vaginal_Swab               |
+|elevated                    |844  |851 |Test_Result                |
+|FSH                         |853  |855 |Hormone_Testing            |
+|menopause                   |875  |883 |Menopause                  |
+
+
+- Assertion Models
+
+|Model Name         | Assertion Status | Description      |
+|-------------------|-------------------|------------------|
+| [assertion_menopause_wip](https://nlp.johnsnowlabs.com/2024/07/03/assertion_menopause_wip_en.html) | `Present`, `Absent`, `Possible`, `Past`, `Hypothetical`, `Planned`, `Family`, `Menarche_Age`, `Menopause_Age` | This model detects the assertion status of menopause-related entities. |
 
 *Example*:
 
 ```python
-regex_matcher_internal = RegexMatcherInternal()\
-    .setInputCols('document')\
-    .setStrategy("MATCH_ALL")\
-    .setOutputCol("regex_matches")\
-    .setExternalRules(path='./rules/regex_rules.txt', delimiter='~')
-
-entityExtractor = TextMatcherInternal()\
-    .setInputCols(["document", "token"])\
-    .setEntities("matcher_drug.csv")\
-    .setOutputCol("matched_text")\
-    .setCaseSensitive(False)\
-    .setDelimiter("#")\
-    .setMergeOverlapping(False)
-
-entityRuler = EntityRulerInternalApproach()\
-    .setInputCols(["document", "token"])\
-    .setOutputCol("entities")\
-    .setPatternsResource("entities.json")\
-    .setCaseSensitive(False)\
-
-text = """ Name: John Smith, Record date: 2093-01-13, MR #719435, John's doctor prescribed aspirin for his heart condition, along with paracetamol for his fever and headache, amoxicillin for his tonsilitis."""
+assertion = AssertionDLModel.pretrained("assertion_menopause_wip", "en", "clinical/models")\
+    .setInputCols(["sentence", "ner_chunk", "embeddings"]) \
+    .setOutputCol("assertion")
 
+sample_texts = ["""A 50-year-old woman, G2P1, presents with symptoms of perimenopause including night sweats, irregular menstruation, and fatigue.She has previously been diagnosed with hypertension. She is taking hormone replacement therapy with estradiol and norethindrone acetate. Recent tests included a bone density scan, which confirmed osteoporosis and showed elevated FSH levels. She also underwent a vaginal swab test for routine screening. Her mother has a history of breast cancer. Her menarche age was 11."""]
 ```
 
 *Result*:
 
-{:.table-model-big}
-| chunk           |   begin |   end | entity   | ner_source    |
-|:----------------|--------:|------:|:---------|:--------------|
-| 2093-01-13      |      32 |    41 | DATE     | regex_matches |
-| aspirin         |      81 |    87 | Drug     | matched_text  |
-| heart condition |      97 |   111 | Disease  | entities      |
-| paracetamol     |     125 |   135 | Drug     | matched_text  |
-| fever           |     145 |   149 | Symptom  | entities      |
-| headache        |     155 |   162 | Symptom  | entities      |
-| amoxicillin     |     165 |   175 | Drug     | matched_text  |
-| tonsilitis      |     185 |   194 | Disease  | entities      |
-
+|chunk                      |begin|end|ner_label                  |assertion   |confidence|
+|---------------------------|-----|---|---------------------------|------------|----------|
+|G2P1                       |21   |24 |G_P                        |Present     |0.9999    |
+|perimenopause              |53   |65 |Perimenopause              |Present     |0.9999    |
+|night sweats               |77   |88 |Other_Symptom              |Present     |0.9997    |
+|irregular menstruation     |91   |112|Irregular_Menstruation     |Present     |0.9997    |
+|fatigue                    |119  |125|Other_Symptom              |Present     |0.9954    |
+|hypertension               |166  |177|Hypertension               |Past        |0.9916    |
+|hormone replacement therapy|194  |220|Hormone_Replacement_Therapy|Present     |0.9988    |
+|estradiol                  |227  |235|Hormone_Replacement_Therapy|Present     |0.9696    |
+|norethindrone acetate      |241  |261|Hormone_Replacement_Therapy|Present     |0.9984    |
+|osteoporosis               |323  |334|Osteoporosis               |Present     |1.0       |
+|elevated                   |347  |354|Test_Result                |Present     |1.0       |
+|FSH                        |356  |358|Hormone_Testing            |Present     |0.9999    |
+|vaginal swab               |389  |400|Vaginal_Swab               |Present     |1.0       |
+|breast cancer              |458  |470|Oncological                |Family      |0.9843    |
+|11                         |494  |495|Age                        |Menarche_Age|0.9891    |
 
 
 </div><div class="h3-box" markdown="1">
 
-#### Introduction of a new parameter `dataSetInfo` to store dataset details for `AssertionDL` and `GenericClassifier` for Traceability
+####  Clinical Document Analysis with One-Liner Pretrained Pipelines for Specific Clinical Tasks and Concepts
+
+We introduce a suite of advanced, hybrid pretrained pipelines, specifically designed to streamline the clinical document analysis process. These pipelines are built upon multiple state-of-the-art (SOTA) pretrained models, delivering a comprehensive solution for quickly extracting vital information.
+
+
+| Model Name                                                            |      Description            |
+|-----------------------------------------------------------------------|-----------------------------|
+| [`ner_deid_context_nameAugmented_pipeline`](https://nlp.johnsnowlabs.com/2024/05/21/ner_deid_context_nameAugmented_pipeline_en.html) | In this pipeline, there are `ner_deid_generic_augmented`, `ner_deid_subentity_augmented`, `ner_deid_name_multilingual_clinical` NER models and several ContextualParser, RegexMatcher, and TextMatcher models were used |
+| [`ner_profiling_vop`](https://nlp.johnsnowlabs.com/2024/06/03/ner_profiling_vop_en.html) | This pipeline can be used to simultaneously evaluate various pre-trained named entity recognition (NER) models, enabling comprehensive analysis of text data pertaining to patient perspectives and experiences, also known as the "Voice of Patients”. |
+| [`ner_profiling_sdoh`](https://nlp.johnsnowlabs.com/2024/06/17/ner_profiling_sdoh_en.html) | This pipeline can be used to simultaneously evaluate various pre-trained named entity recognition (NER) models, enabling comprehensive analysis of text data pertaining to the social determinants of health (SDOH).  When you run this pipeline over your text, you will end up with the predictions coming out of each pretrained clinical NER model trained with the `embeddings_clinical`, which are specifically designed for clinical and biomedical text. |
 
-The parameters from the `Approach` class, utilized during model training, have been added into the `Model` class. These values are now directly stored within the model itself. Additionally, a new parameter named "dataSetInfo"(details regarding the dataset) has been added for `AssertionDL` and `GenericClassifier`.
 
 *Example*:
 
 ```python
-scope_window = [10,10]
-
-assertionStatus = AssertionDLApproach()\
-    .setLabelCol("label")\
-    .setInputCols("document", "chunk", "embeddings")\
-    .setOutputCol("assertion")\
-    .setBatchSize(64)\
-    .setDropout(0.1)\
-    .setLearningRate(0.001)\
-    .setEpochs(5)\
-    .setValidationSplit(0.2)\
-    .setMaxSentLen(250)\
-    ...
-    .setDatasetInfo("i2b2_assertion_sample_short_dataset")
-
-# save trained model and load 
-clinical_assertion = AssertionDLModel.load("./assertion_custom_model") \
-    .setInputCols(["sentence", "ner_chunk", "embeddings"]) \
-    .setOutputCol("assertion")
+from sparknlp.pretrained import PretrainedPipeline
+
+pipeline_sdoh = PretrainedPipeline("ner_profiling_sdoh", "en", "clinical/models")
+
+text = """
+The patient reported experiencing symptoms of anxiety and depression, which have been affecting his quality of life.
+He reported a history of childhood trauma related to violence and abuse in his household, which has contributed to his smoking, alcohol use and current mental health struggles.
+He denied any recent substance use or sexual activity and reported being monogamous in his relationship with his wife.
+The patient is an immigrant and speaks English as a second language.
+He reported difficulty accessing healthcare due to lack of medical insurance.
+He has a herniated disc, hypertension, coronary artery disease (CAD) and diabetes mellitus.
+The patient has a manic disorder, is presently psychotic and shows impulsive behavior. He has been disabled since 2001.
+"""
 
-!cat ./assertion_custom_model/metadata/part-00000
 ```
 
+*Results*:
 
-*Result*:
+```bash
 
-```python
-{
-    "paramMap": {
-        "startCol": "start",
-        "inputCols": ["document","chunk","embeddings"],
-        "learningRate": 0.0010000000474974513,
-        "outputLogsPath": "training_logs/",
-        "storageRef": "clinical",
-        "maxSentLen": 250,
-        "scopeWindow": [10,10],
-        "endCol": "end",
-        "label": "label",
-        "enableOutputLogs": true,
-        "batchSize": 64,
-        "includeConfidence": true,
-        "graphFile": "./tf_graphs/assertion_graph.pb",
-        "epochs": 5,
-        "dropout": 0.10000000149011612,
-        "graphFolder": "./tf_graphs",
-        "outputCol": "assertion",
-        "validationSplit": 0.20000000298023224,
-        "datasetInfo": "i2b2_assertion_sample_short_dataset"
-    }
-}
+******************** ner_sdoh_substance_usage_wip Model Results ******************** 
+
+('smoking', 'Smoking') ('alcohol use', 'Alcohol') ('substance use', 'Substance_Use')
+
+******************** ner_sdoh_health_behaviours_problems_wip Model Results ******************** 
+
+('anxiety', 'Mental_Health') ('depression', 'Mental_Health') ('quality of life', 'Quality_Of_Life') ('mental health struggles', 'Mental_Health') ('sexual activity', 'Sexual_Activity') ('monogamous', 'Sexual_Activity') ('herniated disc', 'Other_Disease') ('hypertension', 'Hypertension') ('coronary artery disease', 'Other_Disease') ('CAD', 'Other_Disease') ('diabetes mellitus', 'Other_Disease') ('manic disorder', 'Mental_Health') ('psychotic', 'Mental_Health') ('impulsive behavior', 'Mental_Health') ('disabled', 'Disability')
+
+******************** ner_jsl_greedy Model Results ******************** 
+
+('anxiety', 'Psychological_Condition') ('depression', 'Psychological_Condition') ('smoking', 'Smoking') ('alcohol', 'Alcohol') ('substance', 'Substance') ('sexual activity', 'Symptom') ('difficulty accessing healthcare', 'Symptom') ('herniated disc', 'Disease_Syndrome_Disorder') ('hypertension', 'Hypertension') ('coronary artery disease', 'Heart_Disease') ('CAD', 'Heart_Disease') ('diabetes mellitus', 'Diabetes') ('manic disorder', 'Psychological_Condition') ('psychotic', 'Psychological_Condition') ('impulsive behavior', 'Symptom')
+
+******************** ner_jsl_enriched Model Results ******************** 
+
+('anxiety', 'Psychological_Condition') ('depression', 'Psychological_Condition') ('smoking', 'Smoking') ('alcohol', 'Alcohol') ('substance', 'Substance') ('difficulty accessing healthcare', 'Symptom') ('lack of medical insurance', 'Symptom') ('herniated disc', 'Disease_Syndrome_Disorder') ('hypertension', 'Hypertension') ('coronary artery disease', 'Heart_Disease') ('CAD', 'Heart_Disease') ('diabetes mellitus', 'Diabetes') ('manic disorder', 'Psychological_Condition') ('psychotic', 'Symptom') ('impulsive behavior', 'Symptom') ('disabled', 'Symptom')
 ```
 
+Please check the [Task Based Clinical Pretrained Pipelines](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/11.3.Task_Based_Clinical_Pretrained_Pipelines.ipynb) Notebook for more information
 
 
 </div><div class="h3-box" markdown="1">
 
-#### Converting Visual NER Annotations to CoNLL Format for Training Text-Based NER Models with Visual Annotations
+#### Formal Release of Oncological Assertion Status Detection and Relation Extraction Models
+
+We are releasing the formal version of the "work-in-progress (WIP)" assertion status detection and relation extraction models in the Oncology domain.
+
+Here is the reference table:
+
+|WIP Version|Formal Version|Task|
+|-----------|--------------|----|
+|assertion_oncology_demographic_binary_wip|[assertion_oncology_demographic_binary](https://nlp.johnsnowlabs.com/2024/07/03/assertion_oncology_demographic_binary_en.html)|Assertion Status Detection|
+|assertion_oncology_family_history_wip|[assertion_oncology_family_history](https://nlp.johnsnowlabs.com/2024/07/03/assertion_oncology_family_history_en.html)|Assertion Status Detection|
+|assertion_oncology_problem_wip|[assertion_oncology_problem](https://nlp.johnsnowlabs.com/2024/07/03/assertion_oncology_problem_en.html)|Assertion Status Detection|
+|assertion_oncology_response_to_treatment_wip|[assertion_oncology_response_to_treatment](https://nlp.johnsnowlabs.com/2024/07/03/assertion_oncology_response_to_treatment_en.html)|Assertion Status Detection|
+|assertion_oncology_smoking_status_wip|[assertion_oncology_smoking_status](https://nlp.johnsnowlabs.com/2024/07/03/assertion_oncology_smoking_status_en.html)|Assertion Status Detection|
+|assertion_oncology_test_binary_wip|[assertion_oncology_test_binary](https://nlp.johnsnowlabs.com/2024/07/03/assertion_oncology_test_binary_en.html)|Assertion Status Detection|
+|assertion_oncology_treatment_binary_wip|[assertion_oncology_treatment_binary](https://nlp.johnsnowlabs.com/2024/07/03/assertion_oncology_treatment_binary_en.html)|Assertion Status Detection|
+|assertion_oncology_wip|[assertion_oncology](https://nlp.johnsnowlabs.com/2024/07/03/assertion_oncology_en.html)|Assertion Status Detection|
+|re_oncology_biomarker_result_wip|[re_oncology_biomarker_result](https://nlp.johnsnowlabs.com/2024/07/03/re_oncology_biomarker_result_en.html)|Relation Extraction|
+|re_oncology_granular_wip|[re_oncology_granular](https://nlp.johnsnowlabs.com/2024/07/03/re_oncology_granular_en.html)|Relation Extraction|
+|re_oncology_location_wip|[re_oncology_location](https://nlp.johnsnowlabs.com/2024/07/03/re_oncology_location_en.html)|Relation Extraction|
+|re_oncology_size_wip|[re_oncology_size](https://nlp.johnsnowlabs.com/2024/07/03/re_oncology_size_en.html)|Relation Extraction|
+|re_oncology_temporal_wip|[re_oncology_temporal](https://nlp.johnsnowlabs.com/2024/07/03/re_oncology_temporal_en.html)|Relation Extraction|
+|re_oncology_test_result_wip|[re_oncology_test_result](https://nlp.johnsnowlabs.com/2024/07/03/re_oncology_test_result_en.html)|Relation Extraction|
+|re_oncology_wip|[re_oncology](https://nlp.johnsnowlabs.com/2024/07/03/re_oncology_en.html)|Relation Extraction (DL)|
+|redl_oncology_biobert_wip|[redl_oncology_biobert](https://nlp.johnsnowlabs.com/2024/07/03/redl_oncology_biobert_en.html)|Relation Extraction (DL)|
+|redl_oncology_biomarker_result_biobert_wip|[redl_oncology_biomarker_result_biobert](https://nlp.johnsnowlabs.com/2024/07/03/redl_oncology_biomarker_result_biobert_en.html)|Relation Extraction (DL)|
+|redl_oncology_granular_biobert_wip|[redl_oncology_granular_biobert](https://nlp.johnsnowlabs.com/2024/07/03/redl_oncology_granular_biobert_en.html)|Relation Extraction (DL)|
+|redl_oncology_location_biobert_wip|[redl_oncology_location_biobert](https://nlp.johnsnowlabs.com/2024/07/03/redl_oncology_location_biobert_en.html)|Relation Extraction (DL)|
+|redl_oncology_size_biobert_wip|[redl_oncology_size_biobert](https://nlp.johnsnowlabs.com/2024/07/03/redl_oncology_size_biobert_en.html)|Relation Extraction (DL)|
+|redl_oncology_temporal_biobert_wip|[redl_oncology_temporal_biobert](https://nlp.johnsnowlabs.com/2024/07/03/redl_oncology_temporal_biobert_en.html)|Relation Extraction (DL)|
+|redl_oncology_test_result_biobert_wip|[redl_oncology_test_result_biobert](https://nlp.johnsnowlabs.com/2024/07/03/redl_oncology_test_result_biobert_en.html)|Relation Extraction (DL)|
+
+
+</div><div class="h3-box" markdown="1">
 
-This module converts Visual NER annotations into the CoNLL format using the JohnSnowLabs NLP Lab. By processing an NLP Lab-exported JSON file containing Visual NER results, it generates a CoNLL file that is suitable for training Named Entity Recognition (NER) models.
+#### 11 New Fine-Tuned Sentence Embedding Models finetuned with medical assertion datasets
+
+Discover our new fine-tuned transformer-based sentence embedding models, meticulously trained on a curated list of clinical and biomedical datasets. These models are specifically optimized for Few-Shot Assertion tasks but are versatile enough to be utilized for other applications, such as Classification and Retrieval-Augmented Generation (RAG). Our collection offers precise and reliable embeddings tailored for various medical domains, significantly enhancing the extraction, analysis, and processing of assertion-related data in healthcare texts.
+
+| Model Name                                                            |      Description            |
+|-----------------------------------------------------------------------|-----------------------------|
+| [`mpnet_embeddings_medical_assertion_i2b2`](https://nlp.johnsnowlabs.com/2024/05/23/mpnet_embeddings_medical_assertion_i2b2_en.html) | Fine-tuned on the i2b2 dataset using the MPNet architecture.|
+| [`mpnet_embeddings_medical_assertion_jsl`](https://nlp.johnsnowlabs.com/2024/05/23/mpnet_embeddings_medical_assertion_jsl_en.html) | Fine-tuned on the in-house dataset using the MPNet architecture. |
+| [`mpnet_embeddings_medical_assertion_oncology`](https://nlp.johnsnowlabs.com/2024/05/23/mpnet_embeddings_medical_assertion_oncology_en.html) | Fine-tuned on the oncology dataset using the MPNet architecture. |
+| [`mpnet_embeddings_medical_assertion_sdoh`](https://nlp.johnsnowlabs.com/2024/05/23/mpnet_embeddings_medical_assertion_sdoh_en.html) | Fine-tuned on the social determinants of health dataset using the MPNet architecture. |
+| [`e5_base_v2_embeddings_medical_assertion_base`](https://nlp.johnsnowlabs.com/2024/07/03/e5_base_v2_embeddings_medical_assertion_base_en.html) | Fine-tuned on the in-house dataset using the E5 architecture. |
+| [`e5_base_v2_embeddings_medical_assertion_jsl`](https://nlp.johnsnowlabs.com/2024/07/03/e5_base_v2_embeddings_medical_assertion_jsl_en.html) | Fine-tuned on the in-house dataset using the E5 architecture. |
+| [`e5_base_v2_embeddings_medical_assertion_i2b2`](https://nlp.johnsnowlabs.com/2024/07/03/e5_base_v2_embeddings_medical_assertion_i2b2_en.html) | Fine-tuned on the i2b2 dataset using the E5 architecture. |
+| [`e5_base_v2_embeddings_medical_assertion_sdoh`](https://nlp.johnsnowlabs.com/2024/07/04/e5_base_v2_embeddings_medical_assertion_sdoh_en.html) | Fine-tuned on the social determinants of health dataset using the E5 architecture. |
+| [`e5_base_v2_embeddings_medical_assertion_smoking`](https://nlp.johnsnowlabs.com/2024/07/03/e5_base_v2_embeddings_medical_assertion_smoking_en.html) | Fine-tuned on the smoking dataset using the E5 architecture. |
+| [`e5_base_v2_embeddings_medical_assertion_oncology`](https://nlp.johnsnowlabs.com/2024/07/03/e5_base_v2_embeddings_medical_assertion_oncology_en.html) | Fine-tuned on the oncology dataset using the E5 architecture. |
+| [`e5_base_v2_embeddings_medical_assertion_radiology`](https://nlp.johnsnowlabs.com/2024/07/03/e5_base_v2_embeddings_medical_assertion_radiology_en.html) | Fine-tuned on the radiology dataset using the E5 architecture. |
 
 
 *Example*:
 
 ```python
-# Import the module
-from sparknlp_jsl.alab import AnnotationLab
-alab = AnnotationLab()
-
-# Download sample Visual NER result JSON file
-!wget -q https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-workshop/master/tutorials/Annotation_Lab/data/alab_visualner_result.json 
-
-# Convert Visual NER annotations to CoNLL format
-df = alab.get_conll_data_from_visualner(
-    input_json_path = "alab_visualner_result.json",
-    output_name = "visual_ner.conll",
-    save_dir  = "exported_conll"
-)
+mpnet_embedding = MPNetEmbeddings.pretrained("mpnet_embeddings_medical_assertion_sdoh", "en", "clinical/models")\
+    .setInputCols(["document"])\
+    .setOutputCol("mpnet_embeddings")
+
+text = [
+    ["I feel a bit drowsy after taking an insulin."],
+    ["Peter Parker is a nice lad and lives in New York"]
+]
 ```
 
+*Result*:
+
+|  embeddings    |
+|---------------------|
+|[{sentence_embeddings, 0, 43, I feel a bit drowsy after taking an insulin., {sentence -> 0}, [-0.09830807, 0.0137982415, -0.051585164, -0.0023749713, -0.017916167, 0.017543513, 0.025593378, 0.05106...|
+|[{sentence_embeddings, 0, 47, Peter Parker is a nice lad and lives in New York, {sentence -> 0}, [-0.10453681, 0.010062916, -0.024983741, 0.009945293, -0.01242009, 0.018787898, 0.039723188, 0.04624...|
 
-</div><div class="h3-box" markdown="1">
 
-####  Performance Analysis of Deidentification Pipelines on Clinical Texts in a Cluster Environment
 
 
-- Deidentification Pipelines Benchmarks
+</div><div class="h3-box" markdown="1">
+
+#### Significantly Faster Vector-DB Based Entity Resolution Models Than Existing Sentence Entity Resolver Models
 
-    This benchmark provides valuable insights into the efficiency and scalability of deidentification pipelines in different computational environments.
+We have developed vector database-based entity resolution models that are 10x faster on GPU and 2x as fast on CPU compared to the existing Sentence Entity Resolver models.
 
-    - **Dataset:** 100000 Clinical Texts from MTSamples, approx. 508 tokens and 26.44 chunks per text.
-    - **Versions:[May-2024]**
-        - **spark-nlp Version:** v5.3.2
-        - **spark-nlp-jsl Version:** v5.3.2
-        - **Spark Version:** v3.4.0
-    - **Instance Type:**
-        - DataBricks Config: 
-            - 32 CPU Core, 128GiB RAM (8 worker) (2.7 $/hr)
+**NOTE:** These models are not available on the Models Hub page yet and cannot be used like the other Spark NLP for Healthcare models. They will be integrated into the marketplace and made available there soon.  
 
-            {:.table-model-big}
-            |data_count |partition |Databricks |
-            |----------:|---------:|----------:|
-            |    100000 |      512 | 1h 42m 55s|
+</div><div class="h3-box" markdown="1">
     
-        - AWS EC2 instance Config:
-                - 8 CPU cores, 58GiB RAM (r6a.2xlarge $0.4536/h)
+#### RxNorm Code Mapping Benchmarks and Cost Comparisons: Healthcare NLP, GPT-4, and Amazon Comprehend Medical
+
+We have prepared an accuracy benchmark and the cost analysis between Healthcare NLP, GPT-4, and Amazon Comprehend Medical for mapping medications to their RxNorm terms. Here are the notes:
+- For the ground truth dataset, we used 79 in-house clinical notes annotated by the medical experts of John Snow Labs.
+- Healthcare NLP: We used `sbiobertresolve_rxnorm_augmented` and `biolordresolve_rxnorm_augmented` models for this benchmark. These models can return up to 25 closest results sorted by their distances.
+- GPT-4: Both GPT-4 (Turbo) and GPT-4o models are used. According to the [official announcement](https://community.openai.com/t/announcing-gpt-4o-in-the-api/744700?page=3), the performance of GPT-4 and GPT-4o is almost identical, and we used both versions for the accuracy calculation. Additionally, the GPT-4 returns **only one result**, which means you will see the same results in both evaluation approaches. 
+- Amazon Comprehend Medical: The RxNorm tool of this service is used, and it returns up to 5 closest matches sorted by their distances.
+- We adopted two approaches for evaluating these tools, given that the model outputs may not precisely match the annotations:
+  - **Top-3:** Compare the annotations to see if they appear in the first three results.
+  - **Top-5:** Compare the annotations to see if they appear in the first five results.
+
+Here are the accuracy results:
+  
+**Top-3 Results:**
+
+![top_3](https://github.com/JohnSnowLabs/spark-nlp-internal/assets/76607915/550d89b5-1c4c-4d40-a5ea-1f9ec86387da)
+
+**Top-5 Results:**
+
+![top_5](https://github.com/JohnSnowLabs/spark-nlp-internal/assets/76607915/44353e0b-c8c1-4570-9cb9-e0a1f59e3dd7)
+
+**Conclusion:**
+
+Based on the evaluation results:
+- The `sbiobertresolve_rxnorm_augmented` model of Healthcare NLP consistently provides **the most accurate** results in each top_k comparison.
+- The `biolordresolve_rxnorm_augmented` model of Healthcare NLP **outperforms** Amazon Comprehend Medical and GPT-4 in mapping terms to their RxNorm codes.
+- The GPT-4 could only return one result, reflected similarly in both charts and has proven to be **the least accurate**.
+
+If you want to process **1M documents** and extract RxNorm codes for medication entities (*excluding the NER stage*), the total cost:
+- With Healthcare NLP is about **$4,500, including the infrastructure costs**.
+- **$24,250** with Amazon Comprehend Medical
+- **$44,000** with the GPT-4 and **$22,000** with the GPT-4o model.
+
+Therefore, **Healthcare NLP is almost 5 times cheaper than its closest alternative**, not to mention the accuracy differences (**Top 3: Healthcare NLP 82.7% vs Amazon 55.8% vs GPT-4 8.9%**).
+
+<table>
+    <caption>Accuracy & Cost Table</caption>
+    <thead>
+      <tr>
+        <th></th>
+        <th>Top-3 Accuracy</th>
+        <th>Top-5 Accuracy</th>
+        <th>Cost</th>
+      </tr>
+    </thead>
+    <tbody>
+      <tr>
+        <td>Healthcare NLP</td>
+        <td>82.7%</td>
+        <td>84.6%</td>
+        <td>$4,500</td>
+      </tr>
+      <tr>
+        <td>Amazon Comprehend Medical</td>
+        <td>55.8%</td>
+        <td>56.2%</td>
+        <td>$24,250</td>
+      </tr>
+      <tr>
+        <td>GPT-4 (Turbo)</td>
+        <td>8.9%</td>
+        <td>8.9%</td>
+        <td>$44,000</td>
+      </tr>
+      <tr>
+        <td>GPT-4o</td>
+        <td>8.9%</td>
+        <td>8.9%</td>
+        <td>$22,000</td>
+      </tr>
+    </tbody>
+  </table>
+  
+</div><div class="h3-box" markdown="1">
 
-            {:.table-model-big}
-            |data_count |partition |   AWS   |
-            |----------:|---------:|--------:|
-            |    100000 |      512 | 3h 3m 40|
+If you want to see more details, please check [Benchmarks Page](https://nlp.johnsnowlabs.com/docs/en/benchmark#rxnorm-benchmark-healthcare-nlp--gpt-4--amazon)
+and [State-of-the-art RxNorm Code Mapping with NLP: Comparative Analysis between the tools by John Snow Labs, Amazon, and GPT-4](https://medium.com/john-snow-labs/state-of-the-art-rxnorm-code-mapping-with-nlp-comparative-analysis-between-the-tools-by-john-snow-794b353dc38e) blog post. 
 
+#### New Blogposts on Using NLP in Opioid Research and Healthcare: Harnessing NLP, Knowledge Graphs, and Regex Techniques for Critical Insights
 
-- Deidentification Pipelines Speed Comparison
+Explore the latest developments in healthcare NLP and Knowledge Graphs through our new blog posts, where we take a deep dive into the innovative technologies and methodologies transforming the medical field. These posts offer insights into how the latest tools are being used to analyze large amounts of unstructured data, identify critical medical assets, and extract meaningful patterns and correlations. Learn how these advances are not only improving our understanding of complex health issues but also contributing to more effective prevention, diagnosis, and treatment strategies.
+
+- [Harnessing the Power of NLP and Knowledge Graphs for Opioid Research](https://medium.com/john-snow-labs/harnessing-the-power-of-nlp-and-knowledge-graphs-for-opioid-research-2be5129d6c61) discusses how Natural Language Processing (NLP) and Knowledge Graphs (KG) are transforming opioid research. By using NLP to process large volumes of unstructured medical data and employing Knowledge Graphs to map intricate relationships, researchers can achieve greater insights into the opioid crisis.
+- [Extracting Critical Insights on Opioid Use Disorder with Healthcare NLP Models](https://medium.com/john-snow-labs/extracting-critical-insights-on-opioid-use-disorder-with-healthcare-nlp-models-72a61a55a77a) discusses how John Snow Labs’ Healthcare NLP models are transforming the extraction of crucial insights on opioid use disorder. These advanced NLP techniques efficiently identify and categorize medical terminology related to opioid addiction, improving clinical understanding and treatment strategies.
+- [Extract Medical Named Entities with Regex in Healthcare NLP at Scale](https://medium.com/john-snow-labs/extract-medical-named-entities-with-regex-in-healthcare-nlp-at-scale-f8d82aa0e826) explains that the RegexMatcherInternal class employs regular expressions to detect and associate specific text patterns with predefined entities like dates, SSNs, and email addresses. This method facilitates targeted entity extraction by matching text patterns to these predefined entities.
+- [Extracting Medical Named Entities with Healthcare NLP’s EntityRulerInternal](https://medium.com/john-snow-labs/extracting-medical-named-entities-with-healthcare-nlps-entityrulerinternal-1eddfb2d0181) explains that EntityRulerInternal in Spark NLP extracts medical entities from text using regex patterns or exact matches defined in JSON or CSV files. This post explains how to set it up and use it in a Healthcare NLP pipeline, with practical examples.
+- [Using Contextual Assertion for Clinical Text Analysis: A Comprehensive Guide](https://medium.com/@yigitgul/using-contextual-assertion-for-clinical-text-analysis-a-comprehensive-guide-57d0973426b9) dive into leveraging Healthcare NLP, a robust NLP library, for clinical text analysis, emphasizing the role of Contextual Assertion. Contextual Assertion markedly enhances the accuracy of detecting negation, possibility, and temporality in medical records. It surpasses deep learning-based assertion status detection in accurately categorizing health conditions. Benchmark comparisons reveal an average F1 score improvement of 10-15%, highlighting the superior precision and reliability of Contextual Assertion in healthcare data analysis.
+- [State-of-the-art RxNorm Code Mapping with NLP: Comparative Analysis between the tools by John Snow Labs, Amazon, and GPT-4](https://medium.com/john-snow-labs/state-of-the-art-rxnorm-code-mapping-with-nlp-comparative-analysis-between-the-tools-by-john-snow-794b353dc38e) compares RxNorm code mapping accuracy and a price analysis between John Snow Labs, GPT-4, and Amazon.
 
-    This benchmark presents a detailed comparison of various deidentification pipelines applied to a dataset of 10,000 custom clinical texts, aiming to anonymize sensitive information for research and analysis. The comparison evaluates the elapsed time and processing stages of different deidentification pipelines. Each pipeline is characterized by its unique combination of Named Entity Recognition (NER), deidentification methods, rule-based NER, clinical embeddings, and chunk merging processes.
-    
-    - **Dataset:** 10K Custom Clinical Texts with 1024 partitions, approx. 500 tokens and 14 chunks per text. 
-    - **Versions:**
-        - **spark-nlp Version:** v5.3.1
-        - **spark-nlp-jsl Version:** v5.3.1
-        - **Spark Version:** v3.4.0
-    - **Instance Type:** 
-        -  8 CPU Cores 52GiB RAM (Colab Pro - High RAM)
-
-{:.table-model-big}
-|Deidentification Pipeline Name                   | Elapsed Time     | Stages           |
-|:------------------------------------------------|-----------------:|:-----------------| 
-|[clinical_deidentification_subentity_optimized](https://nlp.johnsnowlabs.com/2024/03/14/clinical_deidentification_subentity_optimized_en.html)| 67 min 44 seconds| 1 NER, 1 Deidentification, 13 Rule-based NER, 1 clinical embedding, 2 chunk merger  |
-|[clinical_deidentification_generic_optimized](https://nlp.johnsnowlabs.com/2024/03/14/clinical_deidentification_generic_optimized_en.html)    | 68 min 31 seconds| 1 NER, 1 Deidentification, 13 Rule-based NER, 1 clinical embedding, 2 chunk merger  |
-|[clinical_deidentification_generic](https://nlp.johnsnowlabs.com/2024/02/21/clinical_deidentification_generic_en.html)                        | 86 min 24 seconds| 1 NER, 4 Deidentification, 13 Rule-based NER, 1 clinical embedding, 2 chunk merger  |
-|[clinical_deidentification_subentity](https://nlp.johnsnowlabs.com/2024/02/21/clinical_deidentification_subentity_en.html)                    | 99 min 41 seconds| 1 NER, 4 Deidentification, 13 Rule-based NER, 1 clinical embedding, 2 chunk merger  |
-|[clinical_deidentification](https://nlp.johnsnowlabs.com/2024/03/27/clinical_deidentification_en.html)                                        |117 min 44 seconds| 2 NER, 1 Deidentification, 13 Rule-based NER, 1 clinical embedding, 3 chunk merger  |
-|[clinical_deidentification_nameAugmented](https://nlp.johnsnowlabs.com/2024/03/14/clinical_deidentification_subentity_nameAugmented_en.html)  |134 min 27 seconds| 2 NER, 4 Deidentification, 13 Rule-based NER, 1 clinical embedding, 3 chunk merger  |
-|[clinical_deidentification_glove](https://nlp.johnsnowlabs.com/2023/06/17/clinical_deidentification_glove_en.html)                            |146 min 51 seconds| 2 NER, 4 Deidentification,  8 Rule-based NER, 1 clinical embedding, 3 chunk merger  |
-|[clinical_deidentification_obfuscation_small](https://nlp.johnsnowlabs.com/2024/02/09/clinical_deidentification_obfuscation_small_en.html)    |147 min 06 seconds| 1 NER, 1 Deidentification,  2 Rule-based NER, 1 clinical embedding, 1 chunk merger  |
-|[clinical_deidentification_slim](https://nlp.johnsnowlabs.com/2023/06/17/clinical_deidentification_slim_en.html)                              |154 min 37 seconds| 2 NER, 4 Deidentification, 15 Rule-based NER, 1 glove embedding,    3 chunk merger  |
-|[clinical_deidentification_multi_mode_output](https://nlp.johnsnowlabs.com/2024/03/27/clinical_deidentification_multi_mode_output_en.html)    |154 min 50 seconds| 2 NER, 4 Deidentification, 13 Rule-based NER, 1 clinical embedding, 3 chunk merger  |
-|[clinical_deidentification_obfuscation_medium](https://nlp.johnsnowlabs.com/2024/02/09/clinical_deidentification_obfuscation_medium_en.html)  |205 min 40 seconds| 2 NER, 1 Deidentification,  2 Rule-based NER, 1 clinical embedding, 1 chunk merger  |
-
-PS: The reasons pipelines with the same stages have different costs are due to the layers of the NER model and the hardcoded regexes in Deidentification.
-
-Please check [Deidentification Benchmarks](https://nlp.johnsnowlabs.com/docs/en/benchmark#deidentification-benchmarks) for more detail
 
 </div><div class="h3-box" markdown="1">
 
+#### New Notebooks for Medication and Resolutions Concept
+
+To better understand the Medication and Resolutions Concept, the following notebooks have been developed:
 
-#### New Blogposts on Relation Extraction, MedDRA, Response to Treatment, and Pretrained Pipelines.
+- New [Clinical Medication Use Case](https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/43.Clinical_Medication_Use_Case.ipynb) notebook: This notebook is designed to extract and analyze medication information from a clinical dataset. Its purpose is to identify commonly used medications, gather details on dosage, frequency, strength, and route, determine current and past usage, understand pharmacological actions, identify treatment purposes, retrieve relevant codes (RxNorm, NDC, UMLS, SNOMED), and find associated adverse events.
+- New [Resolving Medical Terms to Terminology Codes Directly](https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/3.4.Resolving_Medical_Terms_to_Terminology_Codes_Directly.ipynb) notebook: In this notebook, you will find how to optimize the process to get SentenceEntityResolverModel model outputs.
+- New [Analyse Veterinary Documents with Healthcare NLP](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/44.Analyse_Veterinary_Documents_with_Healthcare_NLP.ipynb) notebook: In this notebook, we use Spark NLP for Healthcare to process veterinary documents. We focus on Named Entity Recognition (NER) to identify entities, Assertion Status to confirm their condition, Relation Extraction to understand their relationships, and Entity Resolution to standardize terms. This helps us efficiently extract and analyze critical information from unstructured veterinary texts.
+
+
+
+
+</div><div class="h3-box" markdown="1">
 
-- [Next-Level Relation Extraction in Healthcare NLP: Introducing New Directional and Contextual Features](https://medium.com/john-snow-labs/next-level-relation-extraction-in-healthcare-nlp-introducing-new-directional-and-contextual-cc54eb68a699)
-- [Clinical Document Analysis with One-Liner Pretrained Pipelines in Healthcare NLP](https://medium.com/john-snow-labs/clinical-document-analysis-with-one-liner-pretrained-pipelines-in-healthcare-nlp-4d468150bd53)
-- [Mapping Medical Terms to MedDRA Ontology Using Healthcare NLP](https://medium.com/john-snow-labs/mapping-medical-terms-to-meddra-ontology-using-healthcare-nlp-f75ea44bdaff)
-- [Response to Cancer Treatment](https://medium.com/john-snow-labs/response-to-cancer-treatment-d7d3b6f40aa3)
+#### Updated Udemy MOOC (Our Online Courses) Notebooks
 
+Recently updated  Udemy MOOC (Massive Online Course) notebooks that focus on using Spark NLP annotators for healthcare applications. These notebooks provide practical examples and exercises for learning how to implement and utilize various Spark NLP tools and techniques specifically designed for processing and analyzing healthcare-related text data. The update might include new features, improvements, or additional content to enhance the learning experience for students and professionals in the healthcare field.
 
-#### Various Core Improvements: Bug Fixes, Enhanced Overall Robustness, and Reliability of Spark NLP for Healthcare
+Please check the [Spark_NLP_Udemy_MOOC](https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/Spark_NLP_Udemy_MOOC/Healthcare_NLP) folder for the all Healthcare MOOC Notebooks
+
+
+</div><div class="h3-box" markdown="1">
+
+#### Various Core Improvements; Bug Fixes, Enhanced Overall Robustness, and Reliability of Spark NLP for Healthcare
+
+- Resolved broken links in healthcare demos
+- Added a unique ID field for each entity  into the result of the `pipeline_ouput_parser` module
+- Fixed deidentification AGE obfuscation hanging issue
+- Added DatasetInfo parameter into the `MedicalNERModel` annotator
 
-- Added training params to trainable annotators within the metadata of the trained models
-- Updated Risk Adjustment module with V28Y24
-- Resolved index issue in `AssertionChunkConverter` annotator and `AnnotationLab.get_assertion_data` modules
-- Resolved saving issue in `Flattener` annotator
 
 </div><div class="h3-box" markdown="1">
 
 #### Updated Notebooks And Demonstrations For making Spark NLP For Healthcare Easier To Navigate And Understand
 
-- New [PipelineTracer and PipelineOutputParser Notebook](https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/11.4.PipelineTracer_and_PipelineOutputParser.ipynb)
-- Updated [Task Based Clinical Pretrained Pipelines Notebook](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/11.3.Task_Based_Clinical_Pretrained_Pipelines.ipynb)
-- Updated [Pretrained Clinical Pipelines Notebook](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/11.Pretrained_Clinical_Pipelines.ipynb)
-- Updated [ADE Demo](https://demo.johnsnowlabs.com/healthcare/ADE/)
-- Updated [NER_POSOLOGY Demo](https://demo.johnsnowlabs.com/healthcare/NER_POSOLOGY/)
-- Updated [NER_RADIOLOGY Demo](https://demo.johnsnowlabs.com/healthcare/NER_RADIOLOGY/)
-- Updated [VOP Demo](https://demo.johnsnowlabs.com/healthcare/VOP/)
-- Updated [SDOH Demo](https://demo.johnsnowlabs.com/healthcare/SDOH/)
-- Updated [ONCOLOGY Demo](https://demo.johnsnowlabs.com/healthcare/ONCOLOGY/)
+- New [Clinical Medication Use Case](https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/43.Clinical_Medication_Use_Case.ipynb) notebook
+- New [Resolving Medical Terms to Terminology Codes Directly](https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/3.4.Resolving_Medical_Terms_to_Terminology_Codes_Directly.ipynb) notebook
+- New [Contextual Assertion](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/2.3.Contextual_Assertion.ipynb) notebook
+- New [VectorDB and PostProcessor for RAG Generative AI](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/45.VectorDB_and_PostProcessor_for_RAG_Generative_AI.ipynb) notebook
+- New [Analyse Veterinary Documents with Healthcare NLP](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/44.Analyse_Veterinary_Documents_with_Healthcare_NLP.ipynb) notebook
+- Updated [FewShot Assertion Classifier](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/2.2.FewShot_Assertion_Classifier.ipynb) notebook
+- New [ALCOHOL SMOKING](https://demo.johnsnowlabs.com/healthcare/ALCOHOL_SMOKING/) Demo
+- New [JSL vs GPT4](https://demo.johnsnowlabs.com/healthcare/JSL_GPT4/) Demo
 
 </div><div class="h3-box" markdown="1">
 
 #### We Have Added And Updated A Substantial Number Of New Clinical Models And Pipelines, Further Solidifying Our Offering In The Healthcare Domain.
 
 
-+ `meddra_llt_snomed_mapper`            
-+ `snomed_meddra_llt_mapper`             
-+ `explain_clinical_doc_sdoh`
-+ `explain_clinical_doc_oncology`
-+ `explain_clinical_doc_granular`            
-+ `explain_clinical_doc_mental_health`   
-+ `ner_medication_generic_pipeline`      
-+ `ner_deid_context_augmented_pipeline`  
-+ `ner_deid_generic_context_augmented_pipeline` 
-+ `ner_deid_subentity_context_augmented_pipeline` 
-+ `biolordresolve_rxnorm_augmented`      
-+ `biolordresolve_umls_general_concepts`  
-+ `biolordresolve_icd10cm_augmented_billable_hcc` 
-+ `sbiobertresolve_snomed_veterinary_wip` 
-+ `sbiobertresolve_umls_general_concepts` 
-+ `biolordresolve_avg_rxnorm_augmented` 
-+ `biolordresolve_snomed_findings_aux_concepts` 
-+ `biolordresolve_cpt_procedures_measurements_augmented` 
++ `pdf_deid_subentity_context_augmented_pipeline`
++ `ner_deid_context_nameAugmented_pipeline`
++ `ner_profiling_vop`      
++ `ner_vop_v2`             
++ `ner_alcohol_smoking`    
++ `sbiobertresolve_snomed_veterinary`
++ `cancer_diagnosis_matcher` 
++ `country_matcher` 
++ `email_matcher`  
++ `phone_matcher` 
++ `state_matcher`  
++ `zip_matcher`
++ `contextual_assertion_someone_else`
++ `contextual_assertion_absent`
++ `contextual_assertion_past` 
++ `ner_alcohol_smoking` 
++ `assertion_alcohol_smoking_wip` 
++ `assertion_alcohol_smoking_general_symptoms_wip` 
++ `re_alcohol_smoking_clinical_wip`
++ `genericclassifier_alcohol_mpnet_wip`
++ `genericclassifier_smoking_mpnet_wip`
++ `ner_menopause_core` 
++ `assertion_menopause_wip` 
++ `fewhot_assertion_jsl_e5_base_v2_jsl`
++ `fewhot_assertion_i2b2_e5_base_v2_i2b2`
++ `fewhot_assertion_sdoh_e5_base_v2_sdoh`
++ `fewhot_assertion_smoking_e5_base_v2_smoking`
++ `fewhot_assertion_oncology_e5_base_v2_oncology`
++ `fewhot_assertion_radiology_e5_base_v2_radiology`
++ `mpnet_embeddings_medical_assertion_i2b2`  
++ `mpnet_embeddings_medical_assertion_jsl`   
++ `mpnet_embeddings_medical_assertion_oncology` 
++ `mpnet_embeddings_medical_assertion_sdoh`    
++ `e5_base_v2_embeddings_medical_assertion_base`
++ `e5_base_v2_embeddings_medical_assertion_jsl` 
++ `e5_base_v2_embeddings_medical_assertion_i2b2` 
++ `e5_base_v2_embeddings_medical_assertion_sdoh`
++ `e5_base_v2_embeddings_medical_assertion_smoking` 
++ `e5_base_v2_embeddings_medical_assertion_oncology` 
++ `e5_base_v2_embeddings_medical_assertion_radiology`
++ `ner_profiling_vop`      
++ `ner_profiling_sdoh`     
++ `ner_profiling_oncology` 
++ `explain_clinical_doc_granular`  
++ `explain_clinical_doc_radiology`     
++ `explain_clinical_doc_medication`      
++ `medication_resolver_pipeline`          
++ `medication_resolver_transform_pipeline`
++ `rxnorm_resolver_pipeline`             
++ `rxnorm_mapper`                         
++ `biolordresolve_rxnorm_augmented`    
++ `sbiobertresolve_rxnorm_augmented`      
++ `sbiobertresolve_umls_clinical_drugs`   
 + `sbiobertresolve_umls_disease_syndrome` 
-+ `sbiobertresolve_umls_findings`
-+ `sbiobertresolve_umls_major_concepts` 
-+ `sbiobertresolve_umls_clinical_drugs` 
-+ `sbiobertresolve_umls_drug_substance` 
-+ `sbiobertresolve_icd9` 
++ `sbiobertresolve_umls_drug_substance`   
++ `sbiobertresolve_umls_findings`         
++ `sbiobertresolve_umls_general_concepts` 
++ `sbiobertresolve_umls_major_concepts`   
++ `clinical_deidentification`
++ `clinical_deidentification_multi_mode_output` 
++ `classifierml_ade` 
++ `assertion_dl_radiology`  
++ `ner_oncology_wip` 
++ `ner_sdoh_access_to_healthcare_wip`  
++ `ner_sdoh_community_condition_wip` 
++ `ner_sdoh_demographics_wip` 
++ `ner_sdoh_health_behaviours_problems_wip` 
++ `ner_sdoh_income_social_status_wip`  
++ `ner_sdoh_slim_wip`  
++ `ner_sdoh_social_environment_wip` 
++ `ner_sdoh_substance_usage_wip`
++ `ner_sdoh_wip`
++ `JSL_MedS_q16_v1`
++ `JSL_MedS_q8_v1`
++ `JSL_MedS_q4_v1`
++ `JSL_MedM_q16_v1`
++ `JSL_MedM_q8_v1`
++ `JSL_MedM_q4_v1`
++ `JSL_MedSNer_ZS_q16_v1`
++ `JSL_MedSNer_ZS_q8_v1`
++ `JSL_MedSNer_ZS_q4_v1`
++ `llama3_160_gguf`
+
+
 
 
 </div><div class="h3-box" markdown="1">
diff --git a/docs/en/spark_nlp_healthcare_versions/release_notes_5_4_0.md b/docs/en/spark_nlp_healthcare_versions/release_notes_5_4_0.md
new file mode 100644
index 0000000000..6c7204655a
--- /dev/null
+++ b/docs/en/spark_nlp_healthcare_versions/release_notes_5_4_0.md
@@ -0,0 +1,1043 @@
+---
+layout: docs
+header: true
+seotitle: Spark NLP for Healthcare | John Snow Labs
+title: Spark NLP for Healthcare Release Notes 5.3.3
+permalink: /docs/en/spark_nlp_healthcare_versions/release_notes_5_4_0
+key: docs-licensed-release-notes
+modify_date: 2024-07-10
+show_nav: true
+sidebar:
+    nav: sparknlp-healthcare
+---
+
+<div class="h3-box" markdown="1">
+
+## 5.4.0
+
+#### Highlights
+
+We are delighted to announce remarkable enhancements and updates in our latest release of Spark NLP for Healthcare. **This release comes with a brand new LLM Loader to allow any size of LLMs in gguf format, a few-shot assertion classifier, contextual assertion detection and a demo to showcase accuracy differences between Healthcare NLP and GPT-4 for information extraction tasks as well as the first Menopause-specific medical models, 82 new and updated clinical pretrained models, and pipelines**.
+ 
++ Introducing a brand new `LLMLoader` annotator to load and run large language models in gguf format. We also announce **9 LLMs at various sizes and quantizations** (3x small size medical summarizer and QA model, 3x medium size general model, and 3x small size zero shot entity extractor)
++ Introducing a brand new `FewshotAssertionClassifier` annotator to train assertion detection models using a few samples with better accuracy 
++ Introducing a rule-based `ContextualAssertion` annotator to detect assertion status using patterns and rules without any training or annotation
++ Introducing `VectorDBPostProcessor` annotator to filter and sort the document splits returned by vector databases in a RAG application
++ Introducing `ContextSplitAssembler` annotator to assemble the document post-processed splits as a context into an LLM stage in a RAG application
++ SNOMED entity resolver model for `Veterinary` domains
++ Voice of the Patients named entity recognition (NER) model
++ New rule-based entity matcher models to customize De-IDentification pipelines
++ New NER, assertion, relation extraction, and classification models to identify `Alcohol` and `Smoking` related Medical Entities
++ New NER and assertion models to extract `Menopause` related entities
++ Clinical document analysis with one-liner pretrained pipelines for specific clinical tasks and concepts
++ Formal release of oncological assertion status detection and relation extraction models
++ 11 new fine-tuned sentence embedding models finetuned with medical assertion datasets
++ Significantly faster vector-db based entity resolution models than existing Sentence Entity Resolver models
++ RxNorm code mapping benchmarks and cost comparisons: Healthcare NLP, GPT-4, and Amazon Comprehend Medical  
++ New blog posts on using NLP in opioid research and healthcare: harnessing NLP, knowledge graphs, and regex techniques for critical insights
++ New notebooks for medication and resolutions concept
++ Updated Udemy MOOC (our online courses) notebooks
++ Various core improvements; bug fixes, enhanced overall robustness and reliability of Spark NLP for Healthcare
+    - Resolved broken links in healthcare demos
+    - Added a unique ID field for each entity  into the result of the `pipeline_ouput_parser` module
+    - Fixed deidentification AGE obfuscation hanging issue
+    - Added DatasetInfo parameter into the `MedicalNERModel` annotator
++ Updated notebooks and demonstrations for making Spark NLP for Healthcare easier to navigate and understand
+    - New [Clinical Medication Use Case](https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/43.Clinical_Medication_Use_Case.ipynb) notebook
+    - New [Resolving Medical Terms to Terminology Codes Directly](https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/3.4.Resolving_Medical_Terms_to_Terminology_Codes_Directly.ipynb) notebook
+    - New [Contextual Assertion](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/2.3.Contextual_Assertion.ipynb) notebook
+    - New [VectorDB and PostProcessor for RAG Generative AI](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/45.VectorDB_and_PostProcessor_for_RAG_Generative_AI.ipynb) notebook
+    - New [Analyse Veterinary Documents with Healthcare NLP](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/44.Analyse_Veterinary_Documents_with_Healthcare_NLP.ipynb) notebook
+    - Updated [FewShot Assertion Classifier](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/2.2.FewShot_Assertion_Classifier.ipynb) notebook
+    - New [ALCOHOL SMOKING](https://demo.johnsnowlabs.com/healthcare/ALCOHOL_SMOKING/) Demo
+    - New [JSL vs GPT4](https://demo.johnsnowlabs.com/healthcare/JSL_GPT4/) Demo to showcase accuracy differences between Healthcare NLP and GPT-4 for information extraction tasks
++ The addition and update of numerous new clinical models and pipelines continue to reinforce our offering in the healthcare domain
+
+These enhancements will elevate your experience with Spark NLP for Healthcare, enabling more efficient, accurate, and streamlined analysis of healthcare-related natural language data.
+
+
+</div><div class="h3-box" markdown="1">
+ 
+#### Introducing a Brand New `LLMLoader` Annotator to Load and Run Large Language Models in GGUF format
+
+`LLMLoader` is designed to interact with a LLMs that are converted into gguf format. This module allows using John Snow Labs' licensed LLMs at various sizes that are finetuned on medical context for certain tasks. It provides various methods for setting parameters, loading models, generating text, and retrieving metadata. The `LLMLoader` includes methods for setting various parameters such as input prefix, suffix, cache prompt, number of tokens to predict, sampling techniques, temperature, penalties, and more. Overall, the `LLMLoader`  provides a flexible and extensible framework for interacting with language models in a Python and Scala environment using PySpark and Java.
+
+*Example*:
+
+```python
+
+from sparknlp_jsl.llm import LLMLoader
+
+llm_loader_pretrained = LLMLoader(spark).pretrained("JSL_MedS_q16_v1", "en", "clinical/models")
+
+llm_loader_pretrained.generate("What is the indication for the drug Methadone?")
+```
+
+*Result*:
+
+```bash
+Methadone is used to treat opioid addiction. It is a long-acting opioid agonist that is used to help individuals who are addicted to short-acting opioids such as heroin or other illicit opioids. It is also used to treat chronic pain in patients who have developed tolerance to other opioids.
+```
+
+
+
+</div><div class="h3-box" markdown="1">
+
+#### Introducing a Brand New `FewshotAssertionClassifier` Annotator to Train Assertion Detection Models Using a Few Samples with Better Accuracy 
+
+The newly refactored `FewShotAssertionClassifierModel` and `FewShotAssertionClassifierApproach` simplify assertion annotation in clinical and biomedical texts. By leveraging sentence embeddings, these models deliver precise assertion annotations and integrate seamlessly with any SparkNLP sentence embedding model.
+
+A key feature is the `FewShotAssertionSentenceConverter`, an annotator that formats documents/sentences and NER chunks for assertion classification, requiring an additional step in the pipeline.
+
+This comprehensive approach significantly enhances the extraction, analysis, and processing of assertion-related data, making it an indispensable tool for healthcare text annotation.
+
+The following table demonstrates the enhanced results achieved using the FewShot Assertion model compared to the traditional AssertionDL model across various datasets. The FewShot Assertion model showcases significant improvements in accuracy scores, particularly in complex medical domains.
+
+| Dataset Name  | AssertionDL  | FewShot Assertion |
+|---------------|--------------|-------------------|
+| radiology     | 0.91         | 0.93              |
+| i2b2          | 0.86         | 0.93              |
+| oncology      | 0.55         | 0.90              |
+| jsl_augmented | 0.85         | 0.90              |
+| smoking       | 0.67         | 0.96              |
+| sdoh          | 0.76         | 0.85              |
+
+![image](https://github.com/JohnSnowLabs/spark-nlp-internal/assets/64752006/9d2e4d5d-b727-45c4-82b5-1b453e373653)
+
+
+|FewShot Assertion Model Name| Predicted Classed |
+|----------------------------|-------------------|
+|[fewhot_assertion_jsl_e5_base_v2_jsl](https://nlp.johnsnowlabs.com/2024/07/03/fewhot_assertion_jsl_e5_base_v2_jsl_en.html) | `Present`, `Absent`, `Possible`, `Planned`, `Past`, `Family`, `Hypothetical`, `SomeoneElse`  |
+|[fewhot_assertion_i2b2_e5_base_v2_i2b2](https://nlp.johnsnowlabs.com/2024/07/03/fewhot_assertion_i2b2_e5_base_v2_i2b2_en.html) | `absent`, `associated_with_someone_else`, `conditional`, `hypothetical`, `possible`, `present`   | 
+|[fewhot_assertion_sdoh_e5_base_v2_sdoh](https://nlp.johnsnowlabs.com/2024/07/04/fewhot_assertion_sdoh_e5_base_v2_sdoh_en.html)|  `Absent`, `Past`, `Present`, `Someone_Else`, `Hypothetical`, `Possible`  | 
+|[fewhot_assertion_smoking_e5_base_v2_smoking](https://nlp.johnsnowlabs.com/2024/07/03/fewhot_assertion_smoking_e5_base_v2_smoking_en.html) | `Present`, `Absent`, `Past`  | 
+|[fewhot_assertion_oncology_e5_base_v2_oncology](https://nlp.johnsnowlabs.com/2024/07/03/fewhot_assertion_oncology_e5_base_v2_oncology_en.html) | `Absent`, `Past`, `Present`, `Family`, `Hypothetical`, `Possible`  | 
+|[fewhot_assertion_radiology_e5_base_v2_radiology](https://nlp.johnsnowlabs.com/2024/07/03/fewhot_assertion_radiology_e5_base_v2_radiology_en.html) | `Confirmed`, `Negative`, `Suspected`| 
+
+*Example*:
+
+```python
+few_shot_assertion_converter = FewShotAssertionSentenceConverter()\
+    .setInputCols(["sentence","token", "ner_jsl_chunk"])\
+    .setOutputCol("assertion_sentence")
+
+e5_embeddings = E5Embeddings.pretrained("e5_base_v2_embeddings_medical_assertion_oncology", "en", "clinical/models")\
+    .setInputCols(["assertion_sentence"])\
+    .setOutputCol("assertion_embedding")
+
+few_shot_assertion_classifier = FewShotAssertionClassifierModel()\
+    .pretrained("fewhot_assertion_oncology_e5_base_v2_oncology", "en", "clinical/models")\
+    .setInputCols(["assertion_embedding"])\
+    .setOutputCol("assertion")
+
+sample_text= """The patient is suspected to have colorectal cancer. Her family history is positive for other cancers. The result of the biopsy was positive. A CT scan was ordered to rule out metastases."""
+```
+
+*Result*:
+
+|    | chunks            |   begin |   end | entities         | assertion   |   confidence |
+|---:|:------------------|--------:|------:|:-----------------|:------------|-------------:|
+|  0 | colorectal cancer |      33 |    49 | Cancer_Dx        | Possible    |     0.581282 |
+|  1 | cancers           |      93 |    99 | Cancer_Dx        | Family      |     0.234656 |
+|  2 | biopsy            |     120 |   125 | Pathology_Test   | Past        |     0.957321 |
+|  3 | positive          |     131 |   138 | Pathology_Result | Present     |     0.956439 |
+|  4 | CT scan           |     143 |   149 | Imaging_Test     | Past        |     0.95717  |
+|  5 | metastases        |     175 |   184 | Metastasis       | Possible    |     0.549866 ||
+
+
+Please check the [FewShot Assertion Classifier](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/2.2.FewShot_Assertion_Classifier.ipynb) Notebook for more information
+
+
+
+</div><div class="h3-box" markdown="1">
+
+#### Introducing a Rule-Based `ContextualAssertion` Annotator to Detect Assertion Status Using Patterns and Rules without any Training or Annotation
+
+Introducing Contextual Assertion which identifies contextual cues within text data, such as negation, uncertainty, etc. It is used for clinical assertion detection, etc. It annotates text chunks with assertions based on configurable rules, prefix and suffix patterns, and exception patterns.
+
+- Dataset: 253 Clinical Texts from in-house dataset
+ 
+|Assertion Label|Contextual Assertion|AssertionDL|
+|---------------|:------------------:|:---------:|
+|Absent         |   0.88             |  0.78  |
+|Past           |   0.77             |  0.65  |
+ 
+
+- Dataset: Used in-house jsl_augmented dataset
+
+|Assertion Label|Contextual Assertion|AssertionDL|
+|---------------|:------------------:|:---------:|
+|Absent         |0.82|0.90|
+|Family         |0.63|0.73|
+|Hypothetical   |0.51|0.69|
+|Past           |0.73|0.77|
+|Planned        |0.57|0.62|
+|Possible       |0.49|0.74|
+|SomeoneElse    |0.61|0.81|
+
+Contextual Assertion, a powerful component within Spark NLP, extends beyond mere negation detection. Its ability to identify and classify a diverse range of contextual cues, including uncertainty, temporality, and sentiment, empowers healthcare professionals to extract deeper meaning from complex medical records.
+
+
+| Model Name                                                            |      Description            |
+|-----------------------------------------------------------------------|-----------------------------|
+| [`contextual_assertion_someone_else`](https://nlp.johnsnowlabs.com/2024/06/26/contextual_assertion_someone_else_en.html) |  Identifies contextual cues within text data to detect `someone else` assertions |
+| [`contextual_assertion_absent`](https://nlp.johnsnowlabs.com/2024/07/03/contextual_assertion_absent_en.html) |  Identifies contextual cues within text data to detect `absent` assertions |
+| [`contextual_assertion_past`](https://nlp.johnsnowlabs.com/2024/07/04/contextual_assertion_past_en.html) |  Identifies contextual cues within text data to detect `past` assertions |
+
+*Example*:
+
+```python
+contextual_assertion = ContextualAssertion() \
+    .setInputCols(["sentence", "token", "ner_chunk"]) \
+    .setOutputCol("assertion") \
+    .setPrefixKeywords(["no", "not"]) \
+    .setSuffixKeywords(["unlikely", "negative", "no"]) \
+    .setPrefixRegexPatterns(["\\b(no|without|denies|never|none|free of|not include)\\b"]) \
+    .setSuffixRegexPatterns(["\\b(free of|negative for|absence of|not|rule out)\\b"]) \
+    .setExceptionKeywords(["without"]) \
+    .setExceptionRegexPatterns(["\\b(not clearly)\\b"]) \
+    .addPrefixKeywords(["negative for", "negative"]) \
+    .addSuffixKeywords(["absent", "neither"]) \
+    .setCaseSensitive(False) \
+    .setPrefixAndSuffixMatch(False) \
+    .setAssertion("absent") \
+    .setScopeWindow([2, 2])\
+    .setIncludeChunkToScope(True)\
+
+example_text = """Patient resting in bed. Patient given azithromycin without any difficulty. Patient has audible wheezing, states chest tightness.
+No evidence of hypertension. Patient denies nausea at this time. zofran declined. Patient is also having intermittent sweating associated with pneumonia.
+"""
+```
+
+*Result*:
+
+| ner_chunk      | begin | end  | ner_label | Assertion |
+|----------------|-------|------|-----------|--------|
+| any difficulty | 59    | 72   | PROBLEM   | absent |
+| hypertension   | 149   | 160  | PROBLEM   | absent |
+| nausea         | 178   | 183  | PROBLEM   | absent |
+| zofran         | 199   | 204  | TREATMENT | absent |
+
+
+Please check the [Contextual Assertion](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/2.3.Contextual_Assertion.ipynb) Notebook for more information
+
+
+
+</div><div class="h3-box" markdown="1">
+ 
+#### Introducing `VectorDBPostProcessor` Annotator to Filter and Sort the Document Splits Returned by `VectorDB` in a RAG Application
+
+The `VectorDBPostProcessor` is a powerful tool designed to filter and sort output from the `VectorDBModel` (our own VectorDB implementations will be released soon). This processor refines `VECTOR_SIMILARITY_RANKINGS` input annotations and outputs enhanced `VECTOR_SIMILARITY_RANKINGS` annotations based on specified criteria.
+
+Key Parameters:
+- `filterBy` (str): Select and prioritize filter options (metadata, diversity_by_threshold). Options can be given as a comma-separated string, determining the filtering order. Default: metadata
+- `sortBy` (str): Select sorting option (ascending, descending, lost_in_the_middle, diversity). Default: ascending
+- `caseSensitive` (bool): Determines if string operators' criteria are case-sensitive. Default: False
+- `diversityThreshold` (float): Sets the threshold for the diversity_by_threshold filter. Default: 0.01
+- maxTopKAfterFiltering (int): Limits the number of annotations returned after filtering. Default: 20
+- `allowZeroContentAfterFiltering` (bool): Determines whether zero annotations are allowed after filtering. Default: False
+
+This processor ensures precise and customizable annotation management, making it an essential component for advanced data processing workflows.
+
+*Example*:
+
+```python
+post_processor = VectorDBPostProcessor() \
+    .setInputCols("vector_db") \
+    .setOutputCol("post") \
+    .setSortBy("ascending")
+    .setMaxTopKAfterFiltering(5)
+    .setFilterBy("metadata") \
+    .setMetadataCriteria([
+        {"field": "pubdate", "fieldType": "date", "operator": "greater_than", "value": "2017 May 11", "dateFormats": ["yyyy MMM dd", "yyyy MMM d"], "converterFallback": "filter"},
+        {"field": "distance", "fieldType": "float", "operator": "less_than", "value": "0.5470"},
+        {"field": "title", "fieldType": "string", "operator": "contains", "matchMode": "any", "matchValues": ["diabetes", "immune system"]}
+      ])
+```
+
+Please check the [VectorDB and PostProcessor for RAG Generative AI](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/45.VectorDB_and_PostProcessor_for_RAG_Generative_AI.ipynb) Notebook for more information
+
+
+
+</div><div class="h3-box" markdown="1">
+
+#### Introducing `ContextSplitAssembler` Annotator to Assemble the Document Post-processed Splits as a Context into an LLM Stage in a RAG Application
+
+The ContextSplitAssembler is a versatile tool designed to work seamlessly with vector databases (our own VectorDB implementations will be released soon) and VectorDBPostProcessor. It combines and organizes annotation results with customizable delimiters and optional splitting.
+
+Key Parameters:
+- `joinString` (str): Specifies the delimiter string inserted between annotations when combining them into a single result. Ensures proper separation and organization. Default: " "
+- `explodeSplits` (bool): Determines whether to split the annotations into separate entries. Default: False
+
+This assembler enhances the management and presentation of annotations, making it an essential tool for advanced data processing workflows.
+
+*Example*:
+
+```python
+context_split_assembler = ( ContextSplitAssembler()
+  .setInputCols("vector_db")
+  .setOutputCol("document")
+  .setJoinString("\n")
+  .setExplodeSplits(False))
+```
+
+Please check the [VectorDB and PostProcessor for RAG Generative AI](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/45.VectorDB_and_PostProcessor_for_RAG_Generative_AI.ipynb) Notebook for more information
+
+
+
+</div><div class="h3-box" markdown="1">
+
+#### SNOMED Entity Resolver Model for `Veterinary` Domains
+
+This advanced model facilitates the mapping of veterinary-related entities and concepts to SNOMED codes using `sbiobert_base_cased_mli` Sentence BERT embeddings. It is trained with an enhanced dataset derived from the `sbiobertresolve_snomed_veterinary_wip` model. The model ensures precise and reliable resolution of veterinary terms to standardized SNOMED codes, aiding in consistent and comprehensive veterinary data documentation and analysis.
+
+*Example*:
+
+```python
+snomed_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_snomed_veterinary", "en", "clinical/models") \
+    .setInputCols(["sentence_embeddings"]) \
+    .setOutputCol("snomed_code")\
+    .setDistanceFunction("EUCLIDEAN")
+text = "The veterinary team is closely monitoring the patient for signs of lymphoblastic lymphoma, a malignant neoplasm of lymphoid origin. They are also treating the patient's osteoarthritis, a degenerative joint disease. Additionally, the team is vigilantly observing the facility for potential outbreaks of mink distemper."
+```
+
+*Result*:
+
+|                              ner_chunk| entity|    snomed_code|                                         description|
+|---------------------------------------|-------|---------------|----------------------------------------------------|
+|                 lymphoblastic lymphoma|PROBLEM|312281000009102|                              lymphoblastic lymphoma|
+|a malignant neoplasm of lymphoid origin|PROBLEM|      443495005|               neoplasm of lymphoid system structure|
+|           the patient's osteoarthritis|PROBLEM|      201826000|                              erosive osteoarthrosis|
+|           a degenerative joint disease|PROBLEM|      201819000|degenerative joint disease involving multiple joints|
+|                         mink distemper|PROBLEM|348361000009108|                                      mink distemper|
+
+Please check the [model card](https://nlp.johnsnowlabs.com/2024/06/10/sbiobertresolve_snomed_veterinary_en.html) for more information
+
+
+
+</div><div class="h3-box" markdown="1">
+    
+#### Voice of the Patients Named Entity Recognition (NER) Model
+
+The Voice of the Patients NER Model is designed to extract healthcare-related terms from patient-generated documents. This model processes the natural language used by patients to identify and categorize medical terms, facilitating better understanding and documentation of patient-reported information.
+
+*Example*:
+
+```python
+ner_model = MedicalNerModel.pretrained("ner_vop_v2", "en", "clinical/models")\
+    .setInputCols(["sentence", "token", "embeddings"])\
+    .setOutputCol("ner")
+
+text = "Hello,I'm 20 year old girl. I'm diagnosed with hyperthyroid 1 month ago. I was feeling weak, light headed,poor digestion, panic attacks, depression, left chest pain, increased heart rate, rapidly weight loss, from 4 months. Because of this, I stayed in the hospital and just discharged from hospital. I had many other blood tests, brain mri, ultrasound scan, endoscopy because of some dumb doctors bcs they were not able to diagnose actual problem."
+```
+
+*Result*:
+
+|chunk               |begin|end |ner_label             |
+|--------------------|-----|----|----------------------|
+|20 year old         |15   |25  |Age                   |
+|girl                |27   |30  |Gender                |
+|hyperthyroid        |52   |63  |Disease               |
+|1 month ago         |65   |75  |DateTime              |
+|weak                |92   |95  |Symptom               |
+|light               |98   |102 |Symptom               |
+|panic attacks       |127  |139 |PsychologicalCondition|
+|depression          |142  |151 |PsychologicalCondition|
+|left                |154  |157 |Laterality            |
+|chest               |159  |163 |BodyPart              |
+|pain                |165  |168 |Symptom               |
+|increased           |171  |179 |TestResult            |
+|heart rate          |181  |190 |VitalTest             |
+|rapidly             |193  |199 |Modifier              |
+|weight loss         |201  |211 |Symptom               |
+|4 months            |220  |227 |Duration              |
+|hospital            |263  |270 |ClinicalDept          |
+|discharged          |281  |290 |AdmissionDischarge    |
+|hospital            |297  |304 |ClinicalDept          |
+|blood tests         |324  |334 |Test                  |
+|brain               |337  |341 |BodyPart              |
+|mri                 |343  |345 |Test                  |
+|ultrasound scan     |348  |362 |Test                  |
+|endoscopy           |365  |373 |Procedure             |
+
+Please check the [model card](https://nlp.johnsnowlabs.com/2024/06/07/ner_vop_v2_en.html) for more information
+
+
+
+</div><div class="h3-box" markdown="1">
+
+#### New Rule-Based Entity Matcher Models to Customise De-IDentification Pipelines
+
+We introduce a suite of text and regex matchers, specifically designed to enhance the deidentification and clinical document understanding process with rule-based methods. 
+
+| Model Name                               |      Description            |
+|------------------------------------------|-----------------------------|
+| [`cancer_diagnosis_matcher`](https://nlp.johnsnowlabs.com/2024/06/17/cancer_diagnosis_matcher_en.html) | This model extracts cancer diagnoses in clinical notes using a rule-based `TextMatcherInternal` annotator. |
+| [`country_matcher`](https://nlp.johnsnowlabs.com/2024/06/19/country_matcher_en.html) | This model extracts countries in clinical notes using a rule-based `TextMatcherInternal` annotator. |
+| [`email_matcher`](https://nlp.johnsnowlabs.com/2024/06/19/email_matcher_en.html) | This model extracts emails in clinical notes using a rule-based `RegexMatcherInternal` annotator. |
+| [`phone_matcher`](https://nlp.johnsnowlabs.com/2024/06/19/phone_matcher_en.html) | This model extracts phone entities in clinical notes using a rule-based `RegexMatcherInternal` annotator. |
+| [`state_matcher`](https://nlp.johnsnowlabs.com/2024/06/19/state_matcher_en.html) | This model extracts states in clinical notes using a rule-based `RegexMatcherInternal` annotator. |
+| [`zip_matcher`](https://nlp.johnsnowlabs.com/2024/06/19/zip_matcher_en.html) | This model extracts zip codes in clinical notes using a rule-based `RegexMatcherInternal` annotator. |
+| [`city_matcher`](https://nlp.johnsnowlabs.com/2024/07/02/city_matcher_en.html) | This model extracts city names in clinical notes using a rule-based `TextMatcherInternal` annotator. |
+
+*Example*:
+
+```python
+text_matcher = TextMatcherInternalModel.pretrained("cancer_diagnosis_matcher", "en", "clinical/models") \
+    .setInputCols(["sentence", "token"])\
+    .setOutputCol("cancer_dx")\
+    .setMergeOverlapping(True)
+
+example_text = """A 65-year-old woman had a history of debulking surgery, bilateral oophorectomy with omentectomy, total anterior hysterectomy with radical pelvic lymph nodes dissection due to ovarian carcinoma (mucinous-type carcinoma, stage Ic) 1 year ago. The patient's medical compliance was poor and failed to complete her chemotherapy (cyclophosphamide 750 mg/m2, carboplatin 300 mg/m2). Recently, she noted a palpable right breast mass, 15 cm in size which nearly occupied the whole right breast in 2 months. Core needle biopsy revealed metaplastic carcinoma. Neoadjuvant chemotherapy with the regimens of Taxotere (75 mg/m2), Epirubicin (75 mg/m2), and Cyclophosphamide (500 mg/m2) was given for 6 cycles with poor response, followed by a modified radical mastectomy (MRM) with dissection of axillary lymph nodes and skin grafting. Postoperatively, radiotherapy was done with 5000 cGy in 25 fractions. The histopathologic examination revealed a metaplastic carcinoma with squamous differentiation associated with adenomyoepithelioma. Immunohistochemistry study showed that the tumor cells are positive for epithelial markers-cytokeratin (AE1/AE3) stain, and myoepithelial markers, including cytokeratin 5/6 (CK 5/6), p63, and S100 stains.  Expressions of hormone receptors, including ER, PR, and Her-2/Neu, were all negative."""
+```
+
+*Result*:
+
+|                  chunk|begin| end|    label|
+|-----------------------|-----|----|---------|
+|      ovarian carcinoma|  176| 192|Cancer_dx|
+|mucinous-type carcinoma|  195| 217|Cancer_dx|
+|  metaplastic carcinoma|  528| 548|Cancer_dx|
+|  metaplastic carcinoma|  937| 957|Cancer_dx|
+|    adenomyoepithelioma| 1005|1023|Cancer_dx|
+
+
+
+</div><div class="h3-box" markdown="1">
+ 
+#### New NER, Assertion, Relation Extraction, and Classification Models to Identify `Alcohol` and `Smoking` Related Medical Entities
+
+A suite of models designed for the identification and analysis of alcohol and smoking related entities in text data. These models include Named Entity Recognition (NER), assertion status, relation extraction, and classification, providing a comprehensive toolkit for analyzing substance use information.
+
+- NER Model
+
+|Model Name         | Predicted Entities | Description      |
+|-------------------|--------------------|------------------|
+| [ner_alcohol_smoking](https://nlp.johnsnowlabs.com/2024/07/01/ner_alcohol_smoking_en.html) | `Drinking_Status`, `Alcohol_Type`, `Smoking_Status`, `Smoking_Type`, `Substance_Duration`, `Substance_Frequency`, `Substance_Quantity`, `Cardiovascular_Issues`, `Respiratory_Issues`, `GUT_Issues`, `Neurologic_Issues`, `Psychiatric_Issues`, `Other_Health_Issues`, `Drinking_Environment`, `Cessation_Treatment`, `Withdrawal_Treatment` | Detects alcohol and smoking related entities within text data |
+
+This `ner_alcohol_smoking` model is designed to detect and label alcohol and smoking-related entities within text data. Alcohol refers to beverages containing ethanol, a psychoactive substance that is widely consumed for its pleasurable effects. Smoking typically involves inhaling smoke from burning tobacco, a highly addictive substance. The model has been trained using advanced deep learning techniques on a diverse range of text sources and can accurately recognize and classify a wide range of entities related to alcohol and smoking. The model’s accuracy and precision have been carefully validated against expert-labeled data to ensure reliable and consistent results.
+
+
+*Example*:
+
+```python
+
+ner_model = MedicalNerModel.pretrained("ner_alcohol_smoking", "en", "clinical/models")\
+    .setInputCols(["sentence", "token","embeddings"])\
+    .setOutputCol("ner")
+
+sample_texts = ["""The outpatient clinic addressed a complaint from the patient regarding severe anxiety and withdrawal symptoms. 
+He disclosed a history of alcohol addiction, with weekly episodes of intense binge drinking over the past decade. 
+However, due to recent challenges in his personal and professional life, he decided to quit drinking cold turkey a week ago. 
+Since then, he has been experiencing escalating symptoms including tremors, sweating, nausea, and severe anxiety. 
+The patient denies recent use drugs or smoking, focusing her struggles solely on alcohol.
+He was placed on CIWA protocol w/ lorazepam for management. Scheduled for cognitive-behavioral therapy (CBT)."""]
+```
+
+*Result*:
+
+|chunk                       |begin|end|ner_label             |
+|----------------------------|-----|---|----------------------|
+|anxiety                     |78   |84 |Psychoneurologic_Issue|
+|alcohol addiction           |138  |154|Drinking_Status       |
+|weekly                      |162  |167|Substance_Frequency   |
+|binge                       |189  |193|Substance_Quantity    |
+|drinking                    |195  |202|Drinking_Status       |
+|over the past decade        |204  |223|Substance_Duration    |
+|drinking                    |319  |326|Drinking_Status       |
+|tremors                     |420  |426|Psychoneurologic_Issue|
+|sweating                    |429  |436|Other_Health_Issues   |
+|nausea                      |439  |444|GUT_Issues            |
+|anxiety                     |458  |464|Psychoneurologic_Issue|
+|smoking                     |507  |513|Smoking_Status        |
+|alcohol                     |549  |555|Drinking_Status       |
+|CIWA                        |575  |578|Withdrawal_Treatment  |
+|lorazepam                   |592  |600|Withdrawal_Treatment  |
+|cognitive-behavioral therapy|632  |659|Cessation_Treatment   |
+|CBT                         |662  |664|Cessation_Treatment   |
+
+- Assertion Models
+
+|Model Name         | Assertion Status | Description      |
+|-------------------|-------------------|------------------|
+| [assertion_alcohol_smoking_wip](https://nlp.johnsnowlabs.com/2024/07/03/assertion_alcohol_smoking_wip_en.html) | `Absent`, `Hypothetical_Possible`, `Past_History`, `Present_Planned` | This model detects the assertion status of entities related to alcohol-smoking. |
+| [assertion_alcohol_smoking_general_symptoms_wip](https://nlp.johnsnowlabs.com/2024/07/03/assertion_alcohol_smoking_general_symptoms_wip_en.html) | `Overdose_Symptom`, `Withdrawal_Symptom` | This model detects the assertion status of general symptoms entity related to alcohol-smoking. |
+
+*Example*:
+
+```python
+assertion = AssertionDLModel.pretrained("assertion_alcohol_smoking_wip", "en", "clinical/models")\
+    .setInputCols(["sentence", "ner_chunk", "embeddings"]) \
+    .setOutputCol("assertion")
+
+sample_texts = ["""Per the patient, the last drink was on ___, prior to admission. The patient admits to having experienced tremors, palpitations, and diaphoresis during the past alcohol withdrawals, but he denies ever having experienced seizures. Mr. ___ did not report experiencing any symptoms of withdrawal throughout his hospital stay, and an examination revealed no evidence of withdrawal.""",
+               """SUBSTANCE ABUSE: The patient admitted to occasional binge drinking, but admitted to normally consuming one pint of liquor a day in the week before her admission. Before she attempted suicide, she was heavily intoxicated and had a high blood alcohol level (BAL). Attending the AA meetings and expressing a desire to keep going to AA to support sobriety were two ways the patient showed motivation to stop drinking. The patient was put on the CIWA protocol upon admission, but no PRN Valium was needed for alcohol withdrawal."""]
+```
+
+*Result*:
+
+|chunk       |begin|end|ner_label             |assertion      |confidence|
+|------------|-----|---|----------------------|---------------|----------|
+|drink       |26   |30 |Drinking_Status       |Past_History   |0.8507    |
+|tremors     |105  |111|Psychoneurologic_Issue|Past_History   |0.9315    |
+|palpitations|114  |125|Cardiovascular_Issues |Past_History   |0.9251    |
+|diaphoresis |132  |142|Other_Health_Issues   |Past_History   |0.9181    |
+|alcohol     |160  |166|Drinking_Status       |Past_History   |0.9109    |
+|seizures    |219  |226|Psychoneurologic_Issue|Absent         |0.5359    |
+|binge       |52   |56 |Substance_Quantity    |Present_Planned|0.5528    |
+|drinking    |58   |65 |Drinking_Status       |Present_Planned|0.5704    |
+|one pint    |103  |110|Substance_Quantity    |Present_Planned|0.6838    |
+|liquor      |115  |120|Alcohol_Type          |Present_Planned|0.6879    |
+|a day       |122  |126|Substance_Frequency   |Present_Planned|0.8029    |
+|suicide     |183  |189|Psychoneurologic_Issue|Past_History   |0.731     |
+|intoxicated |208  |218|Psychoneurologic_Issue|Past_History   |0.7832    |
+|alcohol     |241  |247|Drinking_Status       |Past_History   |0.507     |
+|AA          |276  |277|Cessation_Treatment   |Present_Planned|0.4559    |
+|AA          |329  |330|Cessation_Treatment   |Present_Planned|0.5112    |
+|drinking    |404  |411|Drinking_Status       |Present_Planned|0.5385    |
+|CIWA        |441  |444|Withdrawal_Treatment  |Present_Planned|0.5693    |
+|Valium      |482  |487|Withdrawal_Treatment  |Absent         |0.553     |
+|alcohol     |504  |510|Drinking_Status       |Present_Planned|0.5135    |
+
+- Relation Extraction Model
+
+|Model Name         | Predicted Entities | Description      |
+|-------------------|--------------------|------------------|
+| [re_alcohol_smoking_clinical_wip](https://nlp.johnsnowlabs.com/2024/07/03/re_alcohol_smoking_clinical_wip_en.html) | `is_caused_by`, `is_used_for` | It recognizes relations between treatment cessation and withdrawal with drinking and smoking status, as well as relations between various health issues (Neurologic, Psychiatric, Cardiovascular, Respiratory, GUT, and Other Health Issues) and drinking and smoking status. |
+
+*Example*:
+
+```python
+clinical_re_Model = RelationExtractionModel()\
+    .pretrained("re_alcohol_smoking_clinical_wip", "en", "clinical/models")\
+    .setInputCols(["embeddings", "pos_tags", "ner_chunk", "dependencies"])\
+    .setOutputCol("relations")\
+    .setMaxSyntacticDistance(4)\
+    .setRelationPairs(["Cessation_Treatment-Drinking_Status",
+    "Cessation_Treatment-Smoking_Status",
+    "Respiratory_Issues-Drinking_Status",
+    "Respiratory_Issues-Smoking_Status"])
+
+text = ["""Pulmonary Function Tests: Demonstrates airflow limitation consistent with chronic obstructive pulmonary disease
+ (COPD). Diagnosis: Acute exacerbation of COPD secondary to smoking.
+ Diagnosis: Alcoholic fatty liver disease and smoking-related respiratory symptoms.Management: The patient received alcohol cessation counseling and support services to address her alcohol use disorder. She was also provided with smoking cessation pharmacotherapy and behavioral interventions to help her quit smoking."""]
+```
+
+*Result*:
+
+|    |   sentence |   entity1_begin |   entity1_end | chunk1   | entity1            |   entity2_begin |   entity2_end | chunk2                    | entity2             | relation     |   confidence |
+|---:|-----------:|----------------:|--------------:|:---------|:-------------------|----------------:|--------------:|:--------------------------|:--------------------|:-------------|-------------:|
+|  0 |          2 |             154 |           157 | COPD     | Respiratory_Issues |             172 |           178 | smoking                   | Smoking_Status      | is_caused_by |     0.999902 |
+|  2 |          4 |             297 |           303 | alcohol  | Drinking_Status    |             305 |           324 | cessation counseling      | Cessation_Treatment | is_used_for  |     0.999512 |
+|  3 |          4 |             297 |           303 | alcohol  | Drinking_Status    |             330 |           345 | support services          | Cessation_Treatment | is_used_for  |     0.933377 |
+|  4 |          5 |             411 |           417 | smoking  | Smoking_Status     |             419 |           443 | cessation pharmacotherapy | Cessation_Treatment | is_used_for  |     0.996433 |
+|  5 |          5 |             411 |           417 | smoking  | Smoking_Status     |             449 |           472 | behavioral interventions  | Cessation_Treatment | is_used_for  |     0.9565   |
+
+- Classification Models
+
+|Model Name         | Assertion Status | Description      |
+|-------------------|-------------------|------------------|
+| [genericclassifier_alcohol_mpnet_wip](https://nlp.johnsnowlabs.com/2024/07/01/genericclassifier_alcohol_mpnet_wip_en.html) | `Current_Drinker`, `Others` | The primary goal of the model is to categorize texts into two main label categories: 'Current_Drinker' and 'Others.' (past or non-smoker) |
+| [genericclassifier_smoking_mpnet_wip](https://nlp.johnsnowlabs.com/2024/07/01/genericclassifier_smoking_mpnet_wip_en.html) | `Current_Smoker`, `Others` | The primary goal of the model is to categorize texts into two main label categories: 'Current_Smoker' and 'Others.' (past or non-smoker) |
+
+*Example*:
+
+```python
+generic_classifier = GenericClassifierModel.pretrained('genericclassifier_alcohol_mpnet_wip', 'en', 'clinical/models')\
+    .setInputCols("features")\
+    .setOutputCol("prediction")
+
+text_list = [
+             "The patient, with a history of COPD and alcohol dependence, was initially admitted due to a COPD exacerbation and community-acquired pneumonia. The situation was further complicated by alcohol withdrawal. He was later transferred to another facility for treatment of left hand cellulitis, which raised concerns for necrotizing fasciitis.",
+             "Until recently, the patient had maintained stability on his antidepressant regimen. However, he experienced a notable worsening of depressive symptoms last week, leading him to engage in heavy binge drinking as an ineffective way to suppress his emotional distress and feelings of despair.",
+             "Ms. Jane Doe, a 60-year-old retired teacher, presented to the emergency department complaining of severe abdominal pain and vomiting. She has a history of gallstones but has been asymptomatic for years. Currently, she does not smoke or drink alcohol, focusing on a healthy lifestyle.",
+             "Mr. John Smith, a 45-year-old accountant, came to the clinic reporting intense chest pain and shortness of breath. He has a history of hypertension but has managed it well with medication. He currently does not smoke or drink alcohol, maintaining a healthy lifestyle."]
+```
+
+*Result*:
+
+|                                                                                                text|           result|
+|----------------------------------------------------------------------------------------------------|-----------------|
+|The patient, with a history of COPD and alcohol dependence, was initially admitted due to a COPD ...|Current_Drinker|
+|Until recently, the patient had maintained stability on his antidepressant regimen. However, he e...|Current_Drinker|
+|Ms. Jane Doe, a 60-year-old retired teacher, presented to the emergency department complaining of...|         Others|
+|Mr. John Smith, a 45-year-old accountant, came to the clinic reporting intense chest pain and sho...|         Others|
+
+
+</div><div class="h3-box" markdown="1">
+ 
+#### New NER and Assertion Models to Extract `Menopause` Related Entities
+
+A set of sophisticated models aimed at extracting and analyzing menopause-related entities in text data. These models include a Named Entity Recognition (NER) model and assertion models, which identify and determine the status of various menopause-related terms, aiding in comprehensive menopause data analysis.
+
+- NER Model
+
+|Model Name         | Predicted Entities | Description      |
+|-------------------|--------------------|------------------|
+| [ner_menopause_core](https://nlp.johnsnowlabs.com/2024/07/03/ner_menopause_core_en.html) | `Perimenopause`, `Menopause`, `Gynecological_Symptom`, `Gynecological_Disease`, `Other_Symptom`, `Irregular_Menstruation`, `G_P`, `Hypertension`, `Osteoporosis`, `Oncological`, `Fracture`, `Hormone_Replacement_Therapy`, `Osteporosis_Therapy`, `Antidepressants`, `Procedure`, `Hormone_Testing`, `Vaginal_Swab`, `Age`, `Test_Result` | Detects menopause related entities within text data |
+
+This `ner_menopause_core` model is designed to detect and label core entities related to menopause and associated conditions within text data. Menopause-related terms and conditions are crucial factors that influence individuals’ health outcomes, especially among women undergoing the menopausal transition. The model has been trained using advanced machine-learning techniques on a diverse range of text sources. It can accurately recognize and classify a wide range of menopause-related entities. The model’s accuracy and precision have been carefully validated against expert-labeled data to ensure reliable and consistent results.
+
+
+*Example*:
+
+```python
+
+ner_model = MedicalNerModel.pretrained("ner_menopause_core", "en", "clinical/models"))\
+    .setInputCols(["sentence", "token","embeddings"])\
+    .setOutputCol("ner")
+
+sample_texts = ["""The patient is a 52-year-old female, G3P2, who presents with complaints of irregular menstruation and symptoms suggestive of perimenopause. She reports experiencing hot flashes, night sweats, and vaginal dryness. Her medical history includes polycystic ovary syndrome (PCOS), fatigue, mood swings, hypertension diagnosed 5 years ago and currently managed with medication, and osteoporosis diagnosed 2 years ago with ongoing treatment. 
+Current medications include estradiol for hormone replacement therapy, alendronate for osteoporosis therapy, and fluoxetine for depressive symptoms related to menopause. Recent tests and procedures include a bone density scan to monitor osteoporosis, blood tests for estradiol and follicle-stimulating hormone (FSH) levels, and a vaginal swab collected for routine infection screening. Test results showed elevated FSH levels indicating menopause."""]
+```
+
+*Result*:
+
+|chunk                       |begin|end |ner_label                  |
+|----------------------------|-----|----|---------------------------|
+|irregular menstruation      |76   |97  |Irregular_Menstruation     |
+|perimenopause               |126  |138 |Perimenopause              |
+|hot flashes                 |166  |176 |Other_Symptom              |
+|night sweats                |179  |190 |Other_Symptom              |
+|vaginal dryness             |197  |211 |Gynecological_Symptom      |
+|polycystic ovary syndrome   |243  |267 |Gynecological_Disease      |
+|PCOS                        |270  |273 |Gynecological_Disease      |
+|fatigue                     |277  |283 |Other_Symptom              |
+|hypertension                |299  |310 |Hypertension               |
+|osteoporosis                |377  |388 |Osteoporosis               |
+|estradiol                   |466  |474 |Hormone_Replacement_Therapy|
+|hormone replacement therapy |480  |506 |Hormone_Replacement_Therapy|
+|alendronate                 |509  |519 |Osteporosis_Therapy        |
+|osteoporosis                |525  |536 |Osteoporosis               |
+|fluoxetine                  |551  |560 |Antidepressants            |
+|menopause                   |597  |605 |Menopause                  |
+|osteoporosis                |675  |686 |Osteoporosis               |
+|estradiol                   |705  |713 |Hormone_Testing            |
+|follicle-stimulating hormone|719  |746 |Hormone_Testing            |
+|FSH                         |749  |751 |Hormone_Testing            |
+|vaginal swab                |768  |779 |Vaginal_Swab               |
+|elevated                    |844  |851 |Test_Result                |
+|FSH                         |853  |855 |Hormone_Testing            |
+|menopause                   |875  |883 |Menopause                  |
+
+
+- Assertion Models
+
+|Model Name         | Assertion Status | Description      |
+|-------------------|-------------------|------------------|
+| [assertion_menopause_wip](https://nlp.johnsnowlabs.com/2024/07/03/assertion_menopause_wip_en.html) | `Present`, `Absent`, `Possible`, `Past`, `Hypothetical`, `Planned`, `Family`, `Menarche_Age`, `Menopause_Age` | This model detects the assertion status of menopause-related entities. |
+
+*Example*:
+
+```python
+assertion = AssertionDLModel.pretrained("assertion_menopause_wip", "en", "clinical/models")\
+    .setInputCols(["sentence", "ner_chunk", "embeddings"]) \
+    .setOutputCol("assertion")
+
+sample_texts = ["""A 50-year-old woman, G2P1, presents with symptoms of perimenopause including night sweats, irregular menstruation, and fatigue.She has previously been diagnosed with hypertension. She is taking hormone replacement therapy with estradiol and norethindrone acetate. Recent tests included a bone density scan, which confirmed osteoporosis and showed elevated FSH levels. She also underwent a vaginal swab test for routine screening. Her mother has a history of breast cancer. Her menarche age was 11."""]
+```
+
+*Result*:
+
+|chunk                      |begin|end|ner_label                  |assertion   |confidence|
+|---------------------------|-----|---|---------------------------|------------|----------|
+|G2P1                       |21   |24 |G_P                        |Present     |0.9999    |
+|perimenopause              |53   |65 |Perimenopause              |Present     |0.9999    |
+|night sweats               |77   |88 |Other_Symptom              |Present     |0.9997    |
+|irregular menstruation     |91   |112|Irregular_Menstruation     |Present     |0.9997    |
+|fatigue                    |119  |125|Other_Symptom              |Present     |0.9954    |
+|hypertension               |166  |177|Hypertension               |Past        |0.9916    |
+|hormone replacement therapy|194  |220|Hormone_Replacement_Therapy|Present     |0.9988    |
+|estradiol                  |227  |235|Hormone_Replacement_Therapy|Present     |0.9696    |
+|norethindrone acetate      |241  |261|Hormone_Replacement_Therapy|Present     |0.9984    |
+|osteoporosis               |323  |334|Osteoporosis               |Present     |1.0       |
+|elevated                   |347  |354|Test_Result                |Present     |1.0       |
+|FSH                        |356  |358|Hormone_Testing            |Present     |0.9999    |
+|vaginal swab               |389  |400|Vaginal_Swab               |Present     |1.0       |
+|breast cancer              |458  |470|Oncological                |Family      |0.9843    |
+|11                         |494  |495|Age                        |Menarche_Age|0.9891    |
+
+
+</div><div class="h3-box" markdown="1">
+
+####  Clinical Document Analysis with One-Liner Pretrained Pipelines for Specific Clinical Tasks and Concepts
+
+We introduce a suite of advanced, hybrid pretrained pipelines, specifically designed to streamline the clinical document analysis process. These pipelines are built upon multiple state-of-the-art (SOTA) pretrained models, delivering a comprehensive solution for quickly extracting vital information.
+
+
+| Model Name                                                            |      Description            |
+|-----------------------------------------------------------------------|-----------------------------|
+| [`ner_deid_context_nameAugmented_pipeline`](https://nlp.johnsnowlabs.com/2024/05/21/ner_deid_context_nameAugmented_pipeline_en.html) | In this pipeline, there are `ner_deid_generic_augmented`, `ner_deid_subentity_augmented`, `ner_deid_name_multilingual_clinical` NER models and several ContextualParser, RegexMatcher, and TextMatcher models were used |
+| [`ner_profiling_vop`](https://nlp.johnsnowlabs.com/2024/06/03/ner_profiling_vop_en.html) | This pipeline can be used to simultaneously evaluate various pre-trained named entity recognition (NER) models, enabling comprehensive analysis of text data pertaining to patient perspectives and experiences, also known as the "Voice of Patients”. |
+| [`ner_profiling_sdoh`](https://nlp.johnsnowlabs.com/2024/06/17/ner_profiling_sdoh_en.html) | This pipeline can be used to simultaneously evaluate various pre-trained named entity recognition (NER) models, enabling comprehensive analysis of text data pertaining to the social determinants of health (SDOH).  When you run this pipeline over your text, you will end up with the predictions coming out of each pretrained clinical NER model trained with the `embeddings_clinical`, which are specifically designed for clinical and biomedical text. |
+
+
+*Example*:
+
+```python
+from sparknlp.pretrained import PretrainedPipeline
+
+pipeline_sdoh = PretrainedPipeline("ner_profiling_sdoh", "en", "clinical/models")
+
+text = """
+The patient reported experiencing symptoms of anxiety and depression, which have been affecting his quality of life.
+He reported a history of childhood trauma related to violence and abuse in his household, which has contributed to his smoking, alcohol use and current mental health struggles.
+He denied any recent substance use or sexual activity and reported being monogamous in his relationship with his wife.
+The patient is an immigrant and speaks English as a second language.
+He reported difficulty accessing healthcare due to lack of medical insurance.
+He has a herniated disc, hypertension, coronary artery disease (CAD) and diabetes mellitus.
+The patient has a manic disorder, is presently psychotic and shows impulsive behavior. He has been disabled since 2001.
+"""
+
+```
+
+*Results*:
+
+```bash
+
+******************** ner_sdoh_substance_usage_wip Model Results ******************** 
+
+('smoking', 'Smoking') ('alcohol use', 'Alcohol') ('substance use', 'Substance_Use')
+
+******************** ner_sdoh_health_behaviours_problems_wip Model Results ******************** 
+
+('anxiety', 'Mental_Health') ('depression', 'Mental_Health') ('quality of life', 'Quality_Of_Life') ('mental health struggles', 'Mental_Health') ('sexual activity', 'Sexual_Activity') ('monogamous', 'Sexual_Activity') ('herniated disc', 'Other_Disease') ('hypertension', 'Hypertension') ('coronary artery disease', 'Other_Disease') ('CAD', 'Other_Disease') ('diabetes mellitus', 'Other_Disease') ('manic disorder', 'Mental_Health') ('psychotic', 'Mental_Health') ('impulsive behavior', 'Mental_Health') ('disabled', 'Disability')
+
+******************** ner_jsl_greedy Model Results ******************** 
+
+('anxiety', 'Psychological_Condition') ('depression', 'Psychological_Condition') ('smoking', 'Smoking') ('alcohol', 'Alcohol') ('substance', 'Substance') ('sexual activity', 'Symptom') ('difficulty accessing healthcare', 'Symptom') ('herniated disc', 'Disease_Syndrome_Disorder') ('hypertension', 'Hypertension') ('coronary artery disease', 'Heart_Disease') ('CAD', 'Heart_Disease') ('diabetes mellitus', 'Diabetes') ('manic disorder', 'Psychological_Condition') ('psychotic', 'Psychological_Condition') ('impulsive behavior', 'Symptom')
+
+******************** ner_jsl_enriched Model Results ******************** 
+
+('anxiety', 'Psychological_Condition') ('depression', 'Psychological_Condition') ('smoking', 'Smoking') ('alcohol', 'Alcohol') ('substance', 'Substance') ('difficulty accessing healthcare', 'Symptom') ('lack of medical insurance', 'Symptom') ('herniated disc', 'Disease_Syndrome_Disorder') ('hypertension', 'Hypertension') ('coronary artery disease', 'Heart_Disease') ('CAD', 'Heart_Disease') ('diabetes mellitus', 'Diabetes') ('manic disorder', 'Psychological_Condition') ('psychotic', 'Symptom') ('impulsive behavior', 'Symptom') ('disabled', 'Symptom')
+```
+
+Please check the [Task Based Clinical Pretrained Pipelines](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/11.3.Task_Based_Clinical_Pretrained_Pipelines.ipynb) Notebook for more information
+
+
+</div><div class="h3-box" markdown="1">
+
+#### Formal Release of Oncological Assertion Status Detection and Relation Extraction Models
+
+We are releasing the formal version of the "work-in-progress (WIP)" assertion status detection and relation extraction models in the Oncology domain.
+
+Here is the reference table:
+
+|WIP Version|Formal Version|Task|
+|-----------|--------------|----|
+|assertion_oncology_demographic_binary_wip|[assertion_oncology_demographic_binary](https://nlp.johnsnowlabs.com/2024/07/03/assertion_oncology_demographic_binary_en.html)|Assertion Status Detection|
+|assertion_oncology_family_history_wip|[assertion_oncology_family_history](https://nlp.johnsnowlabs.com/2024/07/03/assertion_oncology_family_history_en.html)|Assertion Status Detection|
+|assertion_oncology_problem_wip|[assertion_oncology_problem](https://nlp.johnsnowlabs.com/2024/07/03/assertion_oncology_problem_en.html)|Assertion Status Detection|
+|assertion_oncology_response_to_treatment_wip|[assertion_oncology_response_to_treatment](https://nlp.johnsnowlabs.com/2024/07/03/assertion_oncology_response_to_treatment_en.html)|Assertion Status Detection|
+|assertion_oncology_smoking_status_wip|[assertion_oncology_smoking_status](https://nlp.johnsnowlabs.com/2024/07/03/assertion_oncology_smoking_status_en.html)|Assertion Status Detection|
+|assertion_oncology_test_binary_wip|[assertion_oncology_test_binary](https://nlp.johnsnowlabs.com/2024/07/03/assertion_oncology_test_binary_en.html)|Assertion Status Detection|
+|assertion_oncology_treatment_binary_wip|[assertion_oncology_treatment_binary](https://nlp.johnsnowlabs.com/2024/07/03/assertion_oncology_treatment_binary_en.html)|Assertion Status Detection|
+|assertion_oncology_wip|[assertion_oncology](https://nlp.johnsnowlabs.com/2024/07/03/assertion_oncology_en.html)|Assertion Status Detection|
+|re_oncology_biomarker_result_wip|[re_oncology_biomarker_result](https://nlp.johnsnowlabs.com/2024/07/03/re_oncology_biomarker_result_en.html)|Relation Extraction|
+|re_oncology_granular_wip|[re_oncology_granular](https://nlp.johnsnowlabs.com/2024/07/03/re_oncology_granular_en.html)|Relation Extraction|
+|re_oncology_location_wip|[re_oncology_location](https://nlp.johnsnowlabs.com/2024/07/03/re_oncology_location_en.html)|Relation Extraction|
+|re_oncology_size_wip|[re_oncology_size](https://nlp.johnsnowlabs.com/2024/07/03/re_oncology_size_en.html)|Relation Extraction|
+|re_oncology_temporal_wip|[re_oncology_temporal](https://nlp.johnsnowlabs.com/2024/07/03/re_oncology_temporal_en.html)|Relation Extraction|
+|re_oncology_test_result_wip|[re_oncology_test_result](https://nlp.johnsnowlabs.com/2024/07/03/re_oncology_test_result_en.html)|Relation Extraction|
+|re_oncology_wip|[re_oncology](https://nlp.johnsnowlabs.com/2024/07/03/re_oncology_en.html)|Relation Extraction (DL)|
+|redl_oncology_biobert_wip|[redl_oncology_biobert](https://nlp.johnsnowlabs.com/2024/07/03/redl_oncology_biobert_en.html)|Relation Extraction (DL)|
+|redl_oncology_biomarker_result_biobert_wip|[redl_oncology_biomarker_result_biobert](https://nlp.johnsnowlabs.com/2024/07/03/redl_oncology_biomarker_result_biobert_en.html)|Relation Extraction (DL)|
+|redl_oncology_granular_biobert_wip|[redl_oncology_granular_biobert](https://nlp.johnsnowlabs.com/2024/07/03/redl_oncology_granular_biobert_en.html)|Relation Extraction (DL)|
+|redl_oncology_location_biobert_wip|[redl_oncology_location_biobert](https://nlp.johnsnowlabs.com/2024/07/03/redl_oncology_location_biobert_en.html)|Relation Extraction (DL)|
+|redl_oncology_size_biobert_wip|[redl_oncology_size_biobert](https://nlp.johnsnowlabs.com/2024/07/03/redl_oncology_size_biobert_en.html)|Relation Extraction (DL)|
+|redl_oncology_temporal_biobert_wip|[redl_oncology_temporal_biobert](https://nlp.johnsnowlabs.com/2024/07/03/redl_oncology_temporal_biobert_en.html)|Relation Extraction (DL)|
+|redl_oncology_test_result_biobert_wip|[redl_oncology_test_result_biobert](https://nlp.johnsnowlabs.com/2024/07/03/redl_oncology_test_result_biobert_en.html)|Relation Extraction (DL)|
+
+
+</div><div class="h3-box" markdown="1">
+
+#### 11 New Fine-Tuned Sentence Embedding Models finetuned with medical assertion datasets
+
+Discover our new fine-tuned transformer-based sentence embedding models, meticulously trained on a curated list of clinical and biomedical datasets. These models are specifically optimized for Few-Shot Assertion tasks but are versatile enough to be utilized for other applications, such as Classification and Retrieval-Augmented Generation (RAG). Our collection offers precise and reliable embeddings tailored for various medical domains, significantly enhancing the extraction, analysis, and processing of assertion-related data in healthcare texts.
+
+| Model Name                                                            |      Description            |
+|-----------------------------------------------------------------------|-----------------------------|
+| [`mpnet_embeddings_medical_assertion_i2b2`](https://nlp.johnsnowlabs.com/2024/05/23/mpnet_embeddings_medical_assertion_i2b2_en.html) | Fine-tuned on the i2b2 dataset using the MPNet architecture.|
+| [`mpnet_embeddings_medical_assertion_jsl`](https://nlp.johnsnowlabs.com/2024/05/23/mpnet_embeddings_medical_assertion_jsl_en.html) | Fine-tuned on the in-house dataset using the MPNet architecture. |
+| [`mpnet_embeddings_medical_assertion_oncology`](https://nlp.johnsnowlabs.com/2024/05/23/mpnet_embeddings_medical_assertion_oncology_en.html) | Fine-tuned on the oncology dataset using the MPNet architecture. |
+| [`mpnet_embeddings_medical_assertion_sdoh`](https://nlp.johnsnowlabs.com/2024/05/23/mpnet_embeddings_medical_assertion_sdoh_en.html) | Fine-tuned on the social determinants of health dataset using the MPNet architecture. |
+| [`e5_base_v2_embeddings_medical_assertion_base`](https://nlp.johnsnowlabs.com/2024/07/03/e5_base_v2_embeddings_medical_assertion_base_en.html) | Fine-tuned on the in-house dataset using the E5 architecture. |
+| [`e5_base_v2_embeddings_medical_assertion_jsl`](https://nlp.johnsnowlabs.com/2024/07/03/e5_base_v2_embeddings_medical_assertion_jsl_en.html) | Fine-tuned on the in-house dataset using the E5 architecture. |
+| [`e5_base_v2_embeddings_medical_assertion_i2b2`](https://nlp.johnsnowlabs.com/2024/07/03/e5_base_v2_embeddings_medical_assertion_i2b2_en.html) | Fine-tuned on the i2b2 dataset using the E5 architecture. |
+| [`e5_base_v2_embeddings_medical_assertion_sdoh`](https://nlp.johnsnowlabs.com/2024/07/04/e5_base_v2_embeddings_medical_assertion_sdoh_en.html) | Fine-tuned on the social determinants of health dataset using the E5 architecture. |
+| [`e5_base_v2_embeddings_medical_assertion_smoking`](https://nlp.johnsnowlabs.com/2024/07/03/e5_base_v2_embeddings_medical_assertion_smoking_en.html) | Fine-tuned on the smoking dataset using the E5 architecture. |
+| [`e5_base_v2_embeddings_medical_assertion_oncology`](https://nlp.johnsnowlabs.com/2024/07/03/e5_base_v2_embeddings_medical_assertion_oncology_en.html) | Fine-tuned on the oncology dataset using the E5 architecture. |
+| [`e5_base_v2_embeddings_medical_assertion_radiology`](https://nlp.johnsnowlabs.com/2024/07/03/e5_base_v2_embeddings_medical_assertion_radiology_en.html) | Fine-tuned on the radiology dataset using the E5 architecture. |
+
+
+*Example*:
+
+```python
+mpnet_embedding = MPNetEmbeddings.pretrained("mpnet_embeddings_medical_assertion_sdoh", "en", "clinical/models")\
+    .setInputCols(["document"])\
+    .setOutputCol("mpnet_embeddings")
+
+text = [
+    ["I feel a bit drowsy after taking an insulin."],
+    ["Peter Parker is a nice lad and lives in New York"]
+]
+```
+
+*Result*:
+
+|  embeddings    |
+|---------------------|
+|[{sentence_embeddings, 0, 43, I feel a bit drowsy after taking an insulin., {sentence -> 0}, [-0.09830807, 0.0137982415, -0.051585164, -0.0023749713, -0.017916167, 0.017543513, 0.025593378, 0.05106...|
+|[{sentence_embeddings, 0, 47, Peter Parker is a nice lad and lives in New York, {sentence -> 0}, [-0.10453681, 0.010062916, -0.024983741, 0.009945293, -0.01242009, 0.018787898, 0.039723188, 0.04624...|
+
+
+
+
+</div><div class="h3-box" markdown="1">
+
+#### Significantly Faster Vector-DB Based Entity Resolution Models Than Existing Sentence Entity Resolver Models
+
+We have developed vector database-based entity resolution models that are 10x faster on GPU and 2x as fast on CPU compared to the existing Sentence Entity Resolver models.
+
+**NOTE:** These models are not available on the Models Hub page yet and cannot be used like the other Spark NLP for Healthcare models. They will be integrated into the marketplace and made available there soon.  
+
+</div><div class="h3-box" markdown="1">
+    
+#### RxNorm Code Mapping Benchmarks and Cost Comparisons: Healthcare NLP, GPT-4, and Amazon Comprehend Medical
+
+We have prepared an accuracy benchmark and the cost analysis between Healthcare NLP, GPT-4, and Amazon Comprehend Medical for mapping medications to their RxNorm terms. Here are the notes:
+- For the ground truth dataset, we used 79 in-house clinical notes annotated by the medical experts of John Snow Labs.
+- Healthcare NLP: We used `sbiobertresolve_rxnorm_augmented` and `biolordresolve_rxnorm_augmented` models for this benchmark. These models can return up to 25 closest results sorted by their distances.
+- GPT-4: Both GPT-4 (Turbo) and GPT-4o models are used. According to the [official announcement](https://community.openai.com/t/announcing-gpt-4o-in-the-api/744700?page=3), the performance of GPT-4 and GPT-4o is almost identical, and we used both versions for the accuracy calculation. Additionally, the GPT-4 returns **only one result**, which means you will see the same results in both evaluation approaches. 
+- Amazon Comprehend Medical: The RxNorm tool of this service is used, and it returns up to 5 closest matches sorted by their distances.
+- We adopted two approaches for evaluating these tools, given that the model outputs may not precisely match the annotations:
+  - **Top-3:** Compare the annotations to see if they appear in the first three results.
+  - **Top-5:** Compare the annotations to see if they appear in the first five results.
+
+Here are the accuracy results:
+  
+**Top-3 Results:**
+
+![top_3](https://github.com/JohnSnowLabs/spark-nlp-internal/assets/76607915/550d89b5-1c4c-4d40-a5ea-1f9ec86387da)
+
+**Top-5 Results:**
+
+![top_5](https://github.com/JohnSnowLabs/spark-nlp-internal/assets/76607915/44353e0b-c8c1-4570-9cb9-e0a1f59e3dd7)
+
+**Conclusion:**
+
+Based on the evaluation results:
+- The `sbiobertresolve_rxnorm_augmented` model of Healthcare NLP consistently provides **the most accurate** results in each top_k comparison.
+- The `biolordresolve_rxnorm_augmented` model of Healthcare NLP **outperforms** Amazon Comprehend Medical and GPT-4 in mapping terms to their RxNorm codes.
+- The GPT-4 could only return one result, reflected similarly in both charts and has proven to be **the least accurate**.
+
+If you want to process **1M documents** and extract RxNorm codes for medication entities (*excluding the NER stage*), the total cost:
+- With Healthcare NLP is about **$4,500, including the infrastructure costs**.
+- **$24,250** with Amazon Comprehend Medical
+- **$44,000** with the GPT-4 and **$22,000** with the GPT-4o model.
+
+Therefore, **Healthcare NLP is almost 5 times cheaper than its closest alternative**, not to mention the accuracy differences (**Top 3: Healthcare NLP 82.7% vs Amazon 55.8% vs GPT-4 8.9%**).
+
+<table>
+    <caption>Accuracy & Cost Table</caption>
+    <thead>
+      <tr>
+        <th></th>
+        <th>Top-3 Accuracy</th>
+        <th>Top-5 Accuracy</th>
+        <th>Cost</th>
+      </tr>
+    </thead>
+    <tbody>
+      <tr>
+        <td>Healthcare NLP</td>
+        <td>82.7%</td>
+        <td>84.6%</td>
+        <td>$4,500</td>
+      </tr>
+      <tr>
+        <td>Amazon Comprehend Medical</td>
+        <td>55.8%</td>
+        <td>56.2%</td>
+        <td>$24,250</td>
+      </tr>
+      <tr>
+        <td>GPT-4 (Turbo)</td>
+        <td>8.9%</td>
+        <td>8.9%</td>
+        <td>$44,000</td>
+      </tr>
+      <tr>
+        <td>GPT-4o</td>
+        <td>8.9%</td>
+        <td>8.9%</td>
+        <td>$22,000</td>
+      </tr>
+    </tbody>
+  </table>
+  
+</div><div class="h3-box" markdown="1">
+
+If you want to see more details, please check [Benchmarks Page](https://nlp.johnsnowlabs.com/docs/en/benchmark#rxnorm-benchmark-healthcare-nlp--gpt-4--amazon)
+and [State-of-the-art RxNorm Code Mapping with NLP: Comparative Analysis between the tools by John Snow Labs, Amazon, and GPT-4](https://medium.com/john-snow-labs/state-of-the-art-rxnorm-code-mapping-with-nlp-comparative-analysis-between-the-tools-by-john-snow-794b353dc38e) blog post. 
+
+#### New Blogposts on Using NLP in Opioid Research and Healthcare: Harnessing NLP, Knowledge Graphs, and Regex Techniques for Critical Insights
+
+Explore the latest developments in healthcare NLP and Knowledge Graphs through our new blog posts, where we take a deep dive into the innovative technologies and methodologies transforming the medical field. These posts offer insights into how the latest tools are being used to analyze large amounts of unstructured data, identify critical medical assets, and extract meaningful patterns and correlations. Learn how these advances are not only improving our understanding of complex health issues but also contributing to more effective prevention, diagnosis, and treatment strategies.
+
+- [Harnessing the Power of NLP and Knowledge Graphs for Opioid Research](https://medium.com/john-snow-labs/harnessing-the-power-of-nlp-and-knowledge-graphs-for-opioid-research-2be5129d6c61) discusses how Natural Language Processing (NLP) and Knowledge Graphs (KG) are transforming opioid research. By using NLP to process large volumes of unstructured medical data and employing Knowledge Graphs to map intricate relationships, researchers can achieve greater insights into the opioid crisis.
+- [Extracting Critical Insights on Opioid Use Disorder with Healthcare NLP Models](https://medium.com/john-snow-labs/extracting-critical-insights-on-opioid-use-disorder-with-healthcare-nlp-models-72a61a55a77a) discusses how John Snow Labs’ Healthcare NLP models are transforming the extraction of crucial insights on opioid use disorder. These advanced NLP techniques efficiently identify and categorize medical terminology related to opioid addiction, improving clinical understanding and treatment strategies.
+- [Extract Medical Named Entities with Regex in Healthcare NLP at Scale](https://medium.com/john-snow-labs/extract-medical-named-entities-with-regex-in-healthcare-nlp-at-scale-f8d82aa0e826) explains that the RegexMatcherInternal class employs regular expressions to detect and associate specific text patterns with predefined entities like dates, SSNs, and email addresses. This method facilitates targeted entity extraction by matching text patterns to these predefined entities.
+- [Extracting Medical Named Entities with Healthcare NLP’s EntityRulerInternal](https://medium.com/john-snow-labs/extracting-medical-named-entities-with-healthcare-nlps-entityrulerinternal-1eddfb2d0181) explains that EntityRulerInternal in Spark NLP extracts medical entities from text using regex patterns or exact matches defined in JSON or CSV files. This post explains how to set it up and use it in a Healthcare NLP pipeline, with practical examples.
+- [Using Contextual Assertion for Clinical Text Analysis: A Comprehensive Guide](https://medium.com/@yigitgul/using-contextual-assertion-for-clinical-text-analysis-a-comprehensive-guide-57d0973426b9) dive into leveraging Healthcare NLP, a robust NLP library, for clinical text analysis, emphasizing the role of Contextual Assertion. Contextual Assertion markedly enhances the accuracy of detecting negation, possibility, and temporality in medical records. It surpasses deep learning-based assertion status detection in accurately categorizing health conditions. Benchmark comparisons reveal an average F1 score improvement of 10-15%, highlighting the superior precision and reliability of Contextual Assertion in healthcare data analysis.
+- [State-of-the-art RxNorm Code Mapping with NLP: Comparative Analysis between the tools by John Snow Labs, Amazon, and GPT-4](https://medium.com/john-snow-labs/state-of-the-art-rxnorm-code-mapping-with-nlp-comparative-analysis-between-the-tools-by-john-snow-794b353dc38e) compares RxNorm code mapping accuracy and a price analysis between John Snow Labs, GPT-4, and Amazon.
+
+
+</div><div class="h3-box" markdown="1">
+
+#### New Notebooks for Medication and Resolutions Concept
+
+To better understand the Medication and Resolutions Concept, the following notebooks have been developed:
+
+- New [Clinical Medication Use Case](https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/43.Clinical_Medication_Use_Case.ipynb) notebook: This notebook is designed to extract and analyze medication information from a clinical dataset. Its purpose is to identify commonly used medications, gather details on dosage, frequency, strength, and route, determine current and past usage, understand pharmacological actions, identify treatment purposes, retrieve relevant codes (RxNorm, NDC, UMLS, SNOMED), and find associated adverse events.
+- New [Resolving Medical Terms to Terminology Codes Directly](https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/3.4.Resolving_Medical_Terms_to_Terminology_Codes_Directly.ipynb) notebook: In this notebook, you will find how to optimize the process to get SentenceEntityResolverModel model outputs.
+- New [Analyse Veterinary Documents with Healthcare NLP](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/44.Analyse_Veterinary_Documents_with_Healthcare_NLP.ipynb) notebook: In this notebook, we use Spark NLP for Healthcare to process veterinary documents. We focus on Named Entity Recognition (NER) to identify entities, Assertion Status to confirm their condition, Relation Extraction to understand their relationships, and Entity Resolution to standardize terms. This helps us efficiently extract and analyze critical information from unstructured veterinary texts.
+
+
+
+
+</div><div class="h3-box" markdown="1">
+
+#### Updated Udemy MOOC (Our Online Courses) Notebooks
+
+Recently updated  Udemy MOOC (Massive Online Course) notebooks that focus on using Spark NLP annotators for healthcare applications. These notebooks provide practical examples and exercises for learning how to implement and utilize various Spark NLP tools and techniques specifically designed for processing and analyzing healthcare-related text data. The update might include new features, improvements, or additional content to enhance the learning experience for students and professionals in the healthcare field.
+
+Please check the [Spark_NLP_Udemy_MOOC](https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/Spark_NLP_Udemy_MOOC/Healthcare_NLP) folder for the all Healthcare MOOC Notebooks
+
+
+</div><div class="h3-box" markdown="1">
+
+#### Various Core Improvements; Bug Fixes, Enhanced Overall Robustness, and Reliability of Spark NLP for Healthcare
+
+- Resolved broken links in healthcare demos
+- Added a unique ID field for each entity  into the result of the `pipeline_ouput_parser` module
+- Fixed deidentification AGE obfuscation hanging issue
+- Added DatasetInfo parameter into the `MedicalNERModel` annotator
+
+
+</div><div class="h3-box" markdown="1">
+
+#### Updated Notebooks And Demonstrations For making Spark NLP For Healthcare Easier To Navigate And Understand
+
+- New [Clinical Medication Use Case](https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/43.Clinical_Medication_Use_Case.ipynb) notebook
+- New [Resolving Medical Terms to Terminology Codes Directly](https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/3.4.Resolving_Medical_Terms_to_Terminology_Codes_Directly.ipynb) notebook
+- New [Contextual Assertion](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/2.3.Contextual_Assertion.ipynb) notebook
+- New [VectorDB and PostProcessor for RAG Generative AI](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/45.VectorDB_and_PostProcessor_for_RAG_Generative_AI.ipynb) notebook
+- New [Analyse Veterinary Documents with Healthcare NLP](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/44.Analyse_Veterinary_Documents_with_Healthcare_NLP.ipynb) notebook
+- Updated [FewShot Assertion Classifier](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/2.2.FewShot_Assertion_Classifier.ipynb) notebook
+- New [ALCOHOL SMOKING](https://demo.johnsnowlabs.com/healthcare/ALCOHOL_SMOKING/) Demo
+- New [JSL vs GPT4](https://demo.johnsnowlabs.com/healthcare/JSL_GPT4/) Demo
+
+</div><div class="h3-box" markdown="1">
+
+#### We Have Added And Updated A Substantial Number Of New Clinical Models And Pipelines, Further Solidifying Our Offering In The Healthcare Domain.
+
+
++ `pdf_deid_subentity_context_augmented_pipeline`
++ `ner_deid_context_nameAugmented_pipeline`
++ `ner_profiling_vop`      
++ `ner_vop_v2`             
++ `ner_alcohol_smoking`    
++ `sbiobertresolve_snomed_veterinary`
++ `cancer_diagnosis_matcher` 
++ `country_matcher` 
++ `email_matcher`  
++ `phone_matcher` 
++ `state_matcher`  
++ `zip_matcher`
++ `contextual_assertion_someone_else`
++ `contextual_assertion_absent`
++ `contextual_assertion_past` 
++ `ner_alcohol_smoking` 
++ `assertion_alcohol_smoking_wip` 
++ `assertion_alcohol_smoking_general_symptoms_wip` 
++ `re_alcohol_smoking_clinical_wip`
++ `genericclassifier_alcohol_mpnet_wip`
++ `genericclassifier_smoking_mpnet_wip`
++ `ner_menopause_core` 
++ `assertion_menopause_wip` 
++ `fewhot_assertion_jsl_e5_base_v2_jsl`
++ `fewhot_assertion_i2b2_e5_base_v2_i2b2`
++ `fewhot_assertion_sdoh_e5_base_v2_sdoh`
++ `fewhot_assertion_smoking_e5_base_v2_smoking`
++ `fewhot_assertion_oncology_e5_base_v2_oncology`
++ `fewhot_assertion_radiology_e5_base_v2_radiology`
++ `mpnet_embeddings_medical_assertion_i2b2`  
++ `mpnet_embeddings_medical_assertion_jsl`   
++ `mpnet_embeddings_medical_assertion_oncology` 
++ `mpnet_embeddings_medical_assertion_sdoh`    
++ `e5_base_v2_embeddings_medical_assertion_base`
++ `e5_base_v2_embeddings_medical_assertion_jsl` 
++ `e5_base_v2_embeddings_medical_assertion_i2b2` 
++ `e5_base_v2_embeddings_medical_assertion_sdoh`
++ `e5_base_v2_embeddings_medical_assertion_smoking` 
++ `e5_base_v2_embeddings_medical_assertion_oncology` 
++ `e5_base_v2_embeddings_medical_assertion_radiology`
++ `ner_profiling_vop`      
++ `ner_profiling_sdoh`     
++ `ner_profiling_oncology` 
++ `explain_clinical_doc_granular`  
++ `explain_clinical_doc_radiology`     
++ `explain_clinical_doc_medication`      
++ `medication_resolver_pipeline`          
++ `medication_resolver_transform_pipeline`
++ `rxnorm_resolver_pipeline`             
++ `rxnorm_mapper`                         
++ `biolordresolve_rxnorm_augmented`    
++ `sbiobertresolve_rxnorm_augmented`      
++ `sbiobertresolve_umls_clinical_drugs`   
++ `sbiobertresolve_umls_disease_syndrome` 
++ `sbiobertresolve_umls_drug_substance`   
++ `sbiobertresolve_umls_findings`         
++ `sbiobertresolve_umls_general_concepts` 
++ `sbiobertresolve_umls_major_concepts`   
++ `clinical_deidentification`
++ `clinical_deidentification_multi_mode_output` 
++ `classifierml_ade` 
++ `assertion_dl_radiology`  
++ `ner_oncology_wip` 
++ `ner_sdoh_access_to_healthcare_wip`  
++ `ner_sdoh_community_condition_wip` 
++ `ner_sdoh_demographics_wip` 
++ `ner_sdoh_health_behaviours_problems_wip` 
++ `ner_sdoh_income_social_status_wip`  
++ `ner_sdoh_slim_wip`  
++ `ner_sdoh_social_environment_wip` 
++ `ner_sdoh_substance_usage_wip`
++ `ner_sdoh_wip`
++ `JSL_MedS_q16_v1`
++ `JSL_MedS_q8_v1`
++ `JSL_MedS_q4_v1`
++ `JSL_MedM_q16_v1`
++ `JSL_MedM_q8_v1`
++ `JSL_MedM_q4_v1`
++ `JSL_MedSNer_ZS_q16_v1`
++ `JSL_MedSNer_ZS_q8_v1`
++ `JSL_MedSNer_ZS_q4_v1`
++ `llama3_160_gguf`
+
+
+
+</div><div class="h3-box" markdown="1">
+
+For all Spark NLP for Healthcare models, please check: [Models Hub Page](https://nlp.johnsnowlabs.com/models?edition=Healthcare+NLP)
+
+
+</div><div class="h3-box" markdown="1">
+
+
+
+## Versions
+
+</div>
+{%- include docs-healthcare-pagination.html -%}
\ No newline at end of file