基于深度学习的自然语言处理用于检测紧急患者分拣中的医疗症状和病史
Siryeol Lee1, Juncheol Lee2, Juntae Park3
1Department of Applied Artificial Intelligence, Hanyang University ERICA, Ansan, Republic of Korea.
大型语言模型 (LLM) 可以自动化临床诊断和电子健康记录 (EHR) 在急诊室的记录. 一个KLUE-RoBERTa模型实现了高性能,可解释AI (XAI) 结果得到了医疗专业人员的验证.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
背景情况:
- 手动电子健康记录 (EHR) 文档在急诊室 (ED) 是耗时的.
- 大型语言模型 (LLM) 的进步为临床任务的自动化提供了潜力.
研究的目的:
- 为自动临床诊断和电子健康记录设计和验证LLM.
- 从模拟对话中识别出12种医疗症状和2种患者病史.
主要方法:
- 精心调整了四个预先训练有素的LLM,包括BERT和KLUE-RoBERTa.
- 使用可解释的AI (XAI) 与Shapley增量解释 (SHAP) 进行模型分析.
- 进行了图灵测试,将XAI解释与医务人员的评估进行比较.
主要成果:
- 基于KLUE-RoBERTa的LLM获得了最高的表现 (F1得分:0.965,AUROC:0.893).
- 基于SHAP的XAI显示了高的雅卡德相似性 (0.722) 与医疗工作者的解释.
- 一个图灵测试表明,XAI和人类专家之间的性能差距很小.
结论:
- 在韩国ED中,LLM显示了自动化EHR记录的巨大潜力.
- 克莱尔 - 罗伯塔模型展示了卓越的分类准确性.
- 使用SHAP的XAI提供了可靠的解释,通过图灵测试验证.
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