Dynamic Q A of Clinical Documents with Large Language Models
CoRR(2024)
摘要
Electronic health records (EHRs) house crucial patient data in clinical
notes. As these notes grow in volume and complexity, manual extraction becomes
challenging. This work introduces a natural language interface using large
language models (LLMs) for dynamic question-answering on clinical notes. Our
chatbot, powered by Langchain and transformer-based LLMs, allows users to query
in natural language, receiving relevant answers from clinical notes.
Experiments, utilizing various embedding models and advanced LLMs, show Wizard
Vicuna's superior accuracy, albeit with high compute demands. Model
optimization, including weight quantization, improves latency by approximately
48 times. Promising results indicate potential, yet challenges such as model
hallucinations and limited diverse medical case evaluations remain. Addressing
these gaps is crucial for unlocking the value in clinical notes and advancing
AI-driven clinical decision-making.
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