从临床文本时间序列进行预测:编码器和解码器语言模型家族的适应
Shahriar Noroozizadeh1, Sayantan Kumar2, Jeremy C Weiss2
1School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
ArXiv
|July 30, 2025
概括
通过使用时间顺序的文本数据,从临床笔记中预测患者健康事件得到了改进. 不同的机器学习模型在各种预测任务中表现出色,展示了电子健康记录中的时间信息的价值.
科学领域:
- 人工智能的人工智能
- 临床信息学 临床信息学
- 自然语言处理自然语言处理.
背景情况:
- 临床病例报告包含有价值的时间患者数据,这些数据通常被传统的机器学习不足利用.
- 现有的方法很难利用临床叙述的丰富,顺序性质来完成预测任务.
研究的目的:
- 引入并解决使用临床发现从文本时间序列预测的问题.
- 系统地评估各种机器学习模型用于临床事件预测,时间排序和生存分析.
主要方法:
- 使用通过大型语言模型 (LLM) 辅助管道提取的时间标记临床发现作为主要输入.
- 在预测任务上评估了基于微调解码器的LLM和基于编码器的变压器.
- 进行敏感性分析,比较临床数据的时间排序与文本排序.
主要成果:
- 基于编码器的模型在事件发生预测和时间排序任务上表现出色 (F1得分更高,时间一致性更好).
- 微调的掩盖方法改善了排名表现.
- 调节指令的解码器模型在生存分析的早期预后方面显示出优势.
结论:
- 与非排序文本相比,时间排序的临床数据显著提高了预测性能.
- 不同的模型架构为医疗保健中的各种时间预测任务提供了不同的优势.
- 从临床文本中获取时间信息对医学预测分析的发展具有重大前景.
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