ChronoFormer用时间感知架构提升病历数据预测能力
ChronoFormer: Time-Aware Transformer Architectures for Structured Clinical Event Modeling
- 引入时间嵌入与分层注意力捕捉长期时序依赖
- 在死亡率、再入院等3项任务上超越现有模型
- 适合临床时序建模与医疗预测研究者使用
电子健康记录（EHR）数据的时间复杂性给机器学习预测临床结局带来了重大挑战。本文提出ChronoFormer，一种专为编码和利用纵向患者数据中时序依赖而设计的新型Transformer架构。ChronoFormer融合了时间嵌入、分层注意力机制和领域特定掩码技术。在三个基准任务——死亡率预测、再入院预测和长期共病发生——上的大量实验表明，其性能显著优于当前最先进方法。此外，对注意力模式的详细分析证实，ChronoFormer具备捕捉临床上有意义的长程时序关系的能力。
原文摘要 · Abstract (English)
The temporal complexity of electronic health record (EHR) data presents significant challenges for predicting clinical outcomes using machine learning. This paper proposes ChronoFormer, an innovative transformer based architecture specifically designed to encode and leverage temporal dependencies in longitudinal patient data. ChronoFormer integrates temporal embeddings, hierarchical attention mechanisms, and domain specific masking techniques. Extensive experiments conducted on three benchmark tasks mortality prediction, readmission prediction, and long term comorbidity onset demonstrate substantial improvements over current state of the art methods. Furthermore, detailed analyses of attention patterns underscore ChronoFormer's capability to capture clinically meaningful long range temporal relationships.
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