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利用大型语言模型从临床笔记中推导多发性硬化症进展评估:可行性研究

Sy Hwang¹, Sunil Thomas², Heather Williams²

¹Institute for Biomedical Informatics, Perelman School of Medicine University of Pennsylvania, Philadelphia, PA, USA, sy.hwang@pennmedicine.upenn.edu.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

|February 27, 2026

概括

这项研究使用大型语言模型 (LLM) 来分析多发性硬化症 (MS) 进展的临床笔记. 目标是从患者记录中开发EDSS和FS分数的可行分类器.

科学领域:

神经学神经学
人工智能的人工智能
临床信息学临床信息学

背景情况:

精确评估多发性硬化症 (MS) 进展对于患者护理和研究至关重要.
关键进展指标通常嵌入非结构化的临床笔记中.
目前用于提取这些数据的方法可能是劳动密集型的.

研究的目的:

评估开发和验证基于大型语言模型 (LLM) 的分类器的可行性.
用临床笔记确定多发性硬化症 (MS) 的进展.
为了自动提取扩展残疾状态量表 (EDSS) 和功能系统 (FS) 评分.

主要方法:

开发一个大型语言模型 (LLM) 分类器.
使用临床笔记作为数据源.
验证LLM在分类MS进展指标方面的表现.

主要成果:

该研究评估了基于LLM的方法的可行性.
初步发现表明,可以自动确定MS的进展.
需要进一步验证以确认准确性和可靠性.

结论:

开发基于LLM的分类器是从临床笔记中确定MS进展的可行方法.
这种方法有望提高临床护理和研究的效率.
未来的工作应侧重于强大的验证和整合到临床工作流程中.

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