使用语言模型进行临床风险预测:好处和考虑因素
Angeela Acharya1, Sulabh Shrestha1, Anyi Chen2
1George Mason University, Fairfax, VA, United States.
Journal of the American Medical Informatics Association : JAMIA
|February 27, 2024
概括
语言模型 (LMs) 通过结合领域知识来增强电子健康记录 (EHR) 分析以预测临床风险. 这提高了诊断预测的准确性,并处理新的医学概念,尽管迅速的敏感性需要注意.
科学领域:
- 人工智能在医学中的应用
- 医疗信息学 医疗信息学
- 临床决策支持 临床决策支持
背景情况:
- 电子健康记录 (EHR) 越来越多地用于临床风险预测.
- 有限的特定任务的EHR数据往往阻碍了标准机器学习 (ML) 模型的性能.
- 利用补充领域知识对于改进基于EHR的预测模型至关重要.
研究的目的:
- 研究语言模型 (LMs) 的潜力,以增强用于临床风险预测的EHR数据.
- 开发和评估基于LM的新风险预测方法,使用文本EHR数据进行风险预测.
- 将基于LM的方法与各种数据集的传统方法进行比较.
主要方法:
- 提出了两种基于LM的新方法:LLaMA2-EHR和Sent-e-Med.
- 在结构化的EHR中使用文本描述来预测未来的诊断.
- 在6种方法和3种风险预测任务中进行了比较实验,数据类型和大小各不相同.
主要成果:
- 基于LM的结构化EHR数据的表示,包括诊断史,显著改善了风险预测性能.
- 实现了增强的性能指标,包括接收器操作特征 (ROC) 曲线下的面积和精度回忆 (PR) 曲线.
- 证明了一些好处,例如短时间的学习,处理新的医学概念,以及适应不同医学词汇的能力.
结论:
- 语言模型 (LMs) 拥有广泛的嵌入式知识,在风险预测中对EHR分析非常有价值.
- 由于持续存在的安全问题,LM的应用需要仔细考虑.
- 基于LM的方法为使用EHR数据推进临床风险预测提供了一个有希望的途径.
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