Toolkit for evaluating and monitoring AI models in clinical settings
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Updated
Jul 18, 2024 - Python
Toolkit for evaluating and monitoring AI models in clinical settings
MIMIC-III corpus parsing and section prediction with MedSecId (COLING paper)
🧪Yet Another ICU Benchmark: a holistic framework for the standardization of clinical prediction model experiments. Provide custom datasets, cohorts, prediction tasks, endpoints, preprocessing, and models. Paper: https://arxiv.org/abs/2306.05109
Small footprint info panel that listens, talks and shows useful info on an e-Paper display. 100% local speech transcription and generation.
OMOP standardization pipeline for ICU databases
Provenience of discharge summaries Pythonic access (BioNLP paper)
MIMIC III Corpus Parsing
[arXiv'24] The official implementation code of LEADER.
A program to create RDF and RDF* knowledge graphs from the clinical database MIMIC-III, where a hospital layout has been created and patients move within the hospital.
Repository for the Paper: „On the Importance of Step-wise Embeddings for Heterogeneous Clinical Time-Series“
PyTorch library for MIMIC III Benchmark experiments
The deployment of Artificial Intelligence for the understanding of common text-based tasks in the healthcare sector is key to extraordinary advancement. Electronic Health Records (EHR) are stored in the health care system in an unstructured and categorically distributed way. We have a ICD-9 Labeled data which is being used to find attention on c…
Classification of EHR data with BERT
IJCAI2021: Code for SafeDrug, MIMIC data processing, Medical code mapping
Provenience of discharge summaries
Rare disease identification from free-text clinical notes with ontologies and weak supervision
Your own 🤖 Doctor
Python suite to construct benchmark machine learning datasets from the MIMIC-III 💊 clinical database.
BlueBERT, pre-trained on PubMed abstracts and clinical notes (MIMIC-III).
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