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

• Item type: SIG Technical Reports(1)
• 公開日: 2024-12-05
• タイトル: Optimizing Automatic Summarization of Long Clinical Records Using Dynamic Context Extension Testing and Evaluation of the NBCE Method
• タイトル / 言語: en
• タイトル: Optimizing Automatic Summarization of Long Clinical Records Using Dynamic Context Extension Testing and Evaluation of the NBCE Method
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: 要約
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Social Informatics, Kyoto University
• 著者所属: Medical Informatics, Kyoto University Hospital
• 著者所属: Medical Informatics, Kyoto University Hospital
• 著者所属: Medical Informatics, Kyoto University Hospital
• 著者所属(英): en
• 著者所属(英): Social Informatics, Kyoto University
• 著者所属(英): en
• 著者所属(英): Medical Informatics, Kyoto University Hospital
• 著者所属(英): en
• 著者所属(英): Medical Informatics, Kyoto University Hospital
• 著者所属(英): en
• 著者所属(英): Medical Informatics, Kyoto University Hospital
• 著者名: Guoqing, Zhang; Keita, Fukuyama; Kazumasa, Kishimoto; Tomohiro, Kuroda
• 著者名(英): Guoqing, Zhang; Keita, Fukuyama; Kazumasa, Kishimoto; Tomohiro, Kuroda
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: 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 nearparity with Google's over 175B Gemini on ROUGE-L metrics with 200 samples, indicating strong performance using less resources, enhancing automated EMR summarization feasibility.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: 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 nearparity with Google's over 175B Gemini on ROUGE-L metrics with 200 samples, indicating strong performance using less resources, enhancing automated EMR summarization feasibility.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN10115061
• 書誌情報: 研究報告自然言語処理（NL）; 巻 2024-NL-262, 号 37, p. 1-6, 発行日 2024-12-05
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-8779
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会