Clinical concept annotation with contextual word embedding in active transfer learning environment
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces an active learning method for clinical concept extraction, improving efficiency and accuracy in classifying medical data using models like SCIBERT and CNNs.
Area Of Science
- Natural Language Processing
- Medical Informatics
- Machine Learning
Background
- Unstructured clinical data presents challenges for automated information extraction.
- Accurate identification of clinical concepts (Problem, Treatment, Test) is crucial for healthcare applications.
Purpose Of The Study
- To develop and evaluate an active learning approach for automated clinical concept extraction.
- To classify clinical concepts with high precision and recall from unstructured electronic health records.
Main Methods
- Utilized a lexical-based approach for initial data labeling to support active learning.
- Employed contextual word embedding similarity with BERT variants (ClinicalBERT, DistilBERT, SCIBERT) for concept classification.
- Trained deep learning models, including Convolutional Neural Networks (CNNs), and large language models (LLMs) using active learning.
Main Results
- SCIBERT demonstrated strong performance in active transfer learning, achieving 73.97% F1-score.
- CNNs trained with various embeddings (BERTBase, DistilBERT, SCIBERT, ClinicalBERT) reached 89-93% testing accuracy.
- ClinicalBERT as an LLM achieved the highest performance with 96% testing accuracy.
Conclusions
- The integrated active learning methodology with SCIBERT and CNNs enhances clinical concept extraction.
- The approach improves annotation efficiency and maintains high accuracy, showing significant potential for clinical applications.
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