GaoxiangLuo/LLM-BioMed-NER-RE
[npj Digital Medicine] An In-Depth Evaluation of Federated Learning on Biomedical Natural Language Processing for Information Extraction
This project helps biomedical researchers and clinical informaticists evaluate how well different large language models (LLMs) can extract information from medical texts. It takes raw medical text data and outputs identified diseases, clinical entities, and relationships between them, showing how various LLMs perform on these tasks without requiring extensive model training.
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Use this if you need to compare the effectiveness of different LLMs for automatically identifying diseases and clinical entities or extracting relationships from unstructured biomedical or clinical text.
Not ideal if you're looking for a pre-trained model ready for immediate deployment in a production environment, as this focuses on evaluation rather than offering a direct solution.
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May 01, 2024
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