大型语言多式模式用于新发型2型糖尿病预测,使用五年队列电子健康记录
Jun-En Ding1, Phan Nguyen Minh Thao2, Wen-Chih Peng2
1School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, USA.
Scientific reports
|September 5, 2024
概括
这项研究引入了一个新的AI框架,使用临床笔记和实验室结果来预测2型糖尿病 (T2DM). 整合多种数据源可以提高早期T2DM检测的准确性.
科学领域:
- 医疗保健中的人工智能
- 生物医学信息学 生物医学信息学
- 计算医学是一种计算医学.
背景情况:
- 2型糖尿病 (T2DM) 是全球健康的重大负担.
- 准确的T2DM早期预测对于有效的患者管理和干预至关重要.
- 现有的预测模型往往缺乏全面的数据整合能力.
研究的目的:
- 开发和评估一种新的大型语言多式模式 (LLMM) 框架,用于增强T2DM风险预测.
- 调查整合电子健康记录 (EHR) 的实用性,包括临床笔记和实验室结果,以改善糖尿病检测.
- 评估拟议的LLMM框架在预测新发病T2DM方面的表现.
主要方法:
- 从台湾一家医院收集了5年的 (2017-2021) EHR数据,包括超过140万份临床笔记和387,000份实验室结果.
- 采用文本嵌入编码器和多头注意力来处理实验室值,结合深度神经网络 (DNN) 来整合临床语义.
- 开发了一个LLMM框架,用于合并多式联运数据,包括文本实验室信息,用于风险预测.
主要成果:
- 综合LLMM框架显示,与T2DM早期检测的单模模型相比,其预测能力显著提高.
- 实现了接受器运行特征曲线 (AUC) 下的面积大于0.70用于新发 T2DM 预测.
- 验证了在LLM中利用文字实验室数据的有效性,以提高预测准确度.
结论:
- 拟议的LLMM框架有效地整合了多式联络电子健康记录数据,以改善2型糖尿病风险预测.
- 在LLM中使用文字实验室数据可以提高早期T2DM检测的准确性.
- 这种方法为在糖尿病护理中推进人工智能驱动的预测诊断提供了一个有希望的途径.
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