Optimizing Automatic Summarization of Long Clinical Records Using Dynamic Context Extension:Testing and Evaluation of the NBCE Method

• URL: http://arxiv.org/abs/2411.08586v2
• Date: Thu, 14 Nov 2024 14:07:19 GMT
• Title: Optimizing Automatic Summarization of Long Clinical Records Using Dynamic Context Extension:Testing and Evaluation of the NBCE Method
• Authors: Guoqing Zhang, Keita Fukuyama, Kazumasa Kishimoto, Tomohiro Kuroda,
• Abstract summary: Current manual summarization makes medical staff struggle. We propose an automatic method using LLMs, but long inputs cause LLMs to lose context. We used a 7B model, open-calm-7b, enhanced with Native Bayes Context Extend. Our improved model achieved near parity with Google's over 175B Gemini on ROUGE-L metrics with 200 samples.
• Score: 1.2779169621283721
• License:
• Abstract: Summarizing patient clinical notes is vital for reducing documentation burdens. Current manual summarization makes medical staff struggle. We propose an automatic method using LLMs, but long inputs cause LLMs to lose context, reducing output quality especially in small size model. We used a 7B model, open-calm-7b, enhanced with Native Bayes Context Extend and a redesigned decoding mechanism to reference one sentence at a time, keeping inputs within context windows, 2048 tokens. Our improved model achieved near parity with Google's over 175B Gemini on ROUGE-L metrics with 200 samples, indicating strong performance using less resources, enhancing automated EMR summarization feasibility.

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