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.

翻译：近年来，医疗健康领域经历了数字数据的快速积累，尤其以电子健康记录（EHRs）的形式最为显著。电子健康记录构成了一个宝贵的资源，信息系统（IS）研究人员可将其用于临床应用（例如，发病率预测）。深度学习似乎是利用这一海量数据的自然选择。然而，大量研究表明，深度学习在EHR数据上并未取得其在其他领域那样的成功；简单的模型（如逻辑回归）常常与复杂的深度学习模型表现相当。受此观察启发，我们开发了一种名为理性逻辑回归（RLR）的新模型，该模型以标准逻辑回归（LR）为其特例（因此继承了与EHR数据特性相符的LR归纳偏置）。RLR以有理级数为理论基础，适用于纵向时间序列数据，并能学习可解释的模式。在真实世界临床任务上的实证比较证明了RLR的有效性。