MedSlice: Fine-Tuned Large Language Models for Secure Clinical Note Sectioning

• URL: http://arxiv.org/abs/2501.14105v1
• Date: Thu, 23 Jan 2025 21:32:09 GMT
• Title: MedSlice: Fine-Tuned Large Language Models for Secure Clinical Note Sectioning
• Authors: Joshua Davis, Thomas Sounack, Kate Sciacca, Jessie M Brain, Brigitte N Durieux, Nicole D Agaronnik, Charlotta Lindvall,
• Abstract summary: Fine-tuned open-source LLMs can surpass proprietary models in clinical note sectioning. This study focuses on three sections: History of Present Illness, Interval History, and Assessment and Plan.
• Score: 2.4060718165478376
• License:
• Abstract: Extracting sections from clinical notes is crucial for downstream analysis but is challenging due to variability in formatting and labor-intensive nature of manual sectioning. While proprietary large language models (LLMs) have shown promise, privacy concerns limit their accessibility. This study develops a pipeline for automated note sectioning using open-source LLMs, focusing on three sections: History of Present Illness, Interval History, and Assessment and Plan. We fine-tuned three open-source LLMs to extract sections using a curated dataset of 487 progress notes, comparing results relative to proprietary models (GPT-4o, GPT-4o mini). Internal and external validity were assessed via precision, recall and F1 score. Fine-tuned Llama 3.1 8B outperformed GPT-4o (F1=0.92). On the external validity test set, performance remained high (F1= 0.85). Fine-tuned open-source LLMs can surpass proprietary models in clinical note sectioning, offering advantages in cost, performance, and accessibility.

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