Medical language processing and deep learning techniques have emerged as critical tools for improving healthcare, particularly in the analysis of medical imaging and medical text data. These multimodal data fusion techniques help to improve the interpretation of medical imaging and lead to increased diagnostic accuracy, informed clinical decisions, and improved patient outcomes. The success of these models relies on the ability to extract and consolidate semantic information from clinical text. This paper addresses the need for more robust methods to evaluate the semantic content of medical reports. Conventional natural language processing approaches and metrics are initially designed for considering the semantic context in the natural language domain and machine translation, often failing to capture the complex semantic meanings inherent in medical content. In this study, we introduce a novel approach designed specifically for assessing the semantic similarity between generated medical reports and the ground truth. Our approach is validated, demonstrating its efficiency in assessing domain-specific semantic similarity within medical contexts. By applying our metric to state-of-the-art Chest X-ray report generation models, we obtain results that not only align with conventional metrics but also provide more contextually meaningful scores in the considered medical domain.
Download all the three python scripts (EntityExtractorv2.py, SimilarityScorev2.py, and SimMetric.py)
You may need to install the dictionaries from Spacy and Scispacy.
To install the dictionaries of Scispacy:
pip install scispacy
pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.3/en_core_sci_md-0.5.3.tar.gz # en_core_sci_md
pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.3/en_ner_bionlp13cg_md-0.5.3.tar.gz # en_ner_bionlp13cg_md
To install ``en_core_web_sm" dictionary from Spacy:
pip install -U pip setuptools wheel
pip install -U spacy
python -m spacy download en_core_web_sm
Nltk package is also required: pip install nltk
To evaluate the semantic similarity between "Reference_Text" and "Candidate_Text" try the following commands:
from EntityExtractorv2 import medical_term
from SimMetric import sim_metric
Reference_entities = medical_term(Reference_Text)
Candidate_entities = medical_term(Candidate_Text)
semantic_score = sim_metric(Reference_entities, Candidate_entities)