通过多模式学习和域调整改善临床基础模型,用于慢性疾病预测
IEEE journal of biomedical and health informatics
|August 4, 2025
概括
MsHeCare是一种新的基础模型 (FM),通过整合多模式电子健康记录 (EHR) 来增强慢性疾病的预测. 它通过使用多源域适应 (MSDA) 提高了临床领域的概括性.
科学领域:
- 医疗保健中的人工智能
- 临床信息学 临床信息学
- 机器学习用于预测分析.
背景情况:
- 纵向电子健康记录 (EHR) 对于建模患者轨迹和预测慢性疾病至关重要.
- 基础模型 (FMs) 显示出了解患者健康进展的希望,但在多模式数据集成和域泛化方面面临挑战.
- 现有的方法难以有效地统一多样化的EHR数据,并适应跨临床领域的分布转移.
研究的目的:
- 提出 MsHeCare,一个基于基础模型的框架,用于多模式的 EHR 数据集成和多源域调整 (MSDA).
- 通过合并时间和静态数据来增强个性化的患者表示.
- 改进分布转移的不同临床领域的模型通用性.
主要方法:
- MsHeCare采用两阶段的学习框架:自主监督对比学习的预训练和基于文本的EHR数据的掩面语言建模.
- 交叉注意力机制将时间特征与结构化人口信息集成为统一的患者表征.
- 微调阶段使用MSDA框架,对适应性知识转移进行新型来源重要性估计.
主要成果:
- 在两个真实世界EHR数据集上,MsHeCare显著优于单域基线和最先进的MSDA方法.
- 该模型在不同的目标域数据大小中展示了稳定性.
- 案例研究证实了MsHeCare与临床实践的结合.
结论:
- MsHeCare有效地整合了多模式的EHR数据,并通过MSDA提高了预测性医疗保健的概括性.
- 拟议的框架为多模式,域适应性预测性医疗保健系统提供了一个有希望的解决方案.
- 这种方法有可能推进早期慢性疾病预测和临床决策.
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