用可解释的理性逻辑回归模型，提升医疗数据预测效果。
Interpretable Predictive Models for Healthcare via Rational Logistic Regression
- 基于理性级数构建可解释的逻辑回归模型，适合纵向医疗数据
- 在真实临床任务中表现优于复杂深度学习模型
- 适合需要透明决策过程的医疗场景
近年来，医疗领域数字数据迅速积累，尤其是电子健康记录（EHRs），成为研究人员开展临床应用（如疾病预测）的重要资源。尽管深度学习被视为挖掘海量数据的首选方法，但多项研究表明，其在EHR数据上的表现并不如在其他领域显著；简单模型如逻辑回归常与复杂深度学习模型表现相当。受此启发，本文提出一种新型模型——理性逻辑回归（Rational Logistic Regression, RLR），该模型以标准逻辑回归（LR）为特例，继承了其与EHR数据匹配的归纳偏置。RLR以理性级数为理论基础，适用于纵向时间序列数据，并能学习可解释的模式。在真实临床任务中的实证比较表明，该模型具有良好的有效性。
原文摘要 · Abstract (English)
The healthcare sector has experienced a rapid accumulation of digital data recently, especially in the form of electronic health records (EHRs). EHRs constitute a precious resource that IS researchers could utilize for clinical applications (e.g., morbidity prediction). Deep learning seems like the obvious choice to exploit this surfeit of data. However, numerous studies have shown that deep learning does not enjoy the same kind of success on EHR data as it has in other domains; simple models like logistic regression are frequently as good as sophisticated deep learning ones. Inspired by this observation, we develop a novel model called rational logistic regression (RLR) that has standard logistic regression (LR) as its special case (and thus inherits LR's inductive bias that aligns with EHR data). RLR has rational series as its theoretical underpinnings, works on longitudinal time-series data, and learns interpretable patterns. Empirical comparisons on real-world clinical tasks demonstrate RLR's efficacy.
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