从大语言模型中提炼健康事件预测的知识
Sirui Ding1, Jiancheng Ye2, Xia Hu3
1Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA.
Scientific reports
|December 27, 2024
概括
本研究介绍了CKLE,这是一个使用大语言模型 (LLM) 的框架,用于改进从电子健康记录 (EHR) 进行健康事件预测. 通过将LLM知识提炼成多模式EHR数据,CKLE提高了准确性,优于现有模型.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
背景情况:
- 电子健康记录 (EHR) 对于在重症监护室 (ICU) 预测健康事件至关重要.
- 现有的方法往往侧重于单个数据模式 (文本或结构化数据),限制了预测准确性.
- 整合包括临床文本和结构化数据在内的多模式EHR数据仍然是一个重大挑战.
研究的目的:
- 通过利用大型语言模型 (LLM) 和多模态EHR数据,提出CKLE框架用于健康事件预测.
- 解决LLM在处理非文本数据方面的局限性以及需要本地部署的隐私问题.
- 提高健康事件预测模型的准确性和可解释性.
主要方法:
- CKLE框架将LLM的跨模式知识提炼成使用多模式EHR数据的预测模型.
- 临床文本通过快速学习来改进和增强,并通过LLMs生成嵌入.
- 采用跨模式知识蒸 (KD) 方法,包括对比损失和患者相似性学习.
主要成果:
- 与最先进的模型相比,CKLE在心力衰竭和高血压预测的准确性提高了4.48%.
- 该框架在正常和有限的标签设置中都表现出卓越的性能.
- 功能重要性分析为心脏病预测提供了突出特征的见解,与医学知识保持一致.
结论:
- CKLE有效地利用LLM知识和多模式EHR数据来增强健康事件预测.
- 拟议的跨模式知识蒸方法解决了医疗保健中的LLM可扩展性和可移植性挑战.
- CKLE为现实世界的临床应用提供了一个有前途的方法,提高了预测准确性和可解释性.
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