Unraveling Complex Temporal Patterns in EHRs via Robust Irregular Tensor Factorization.

<i>Electronic health records (EHRs) contain diverse patient data with varying visit frequencies. While irregular tensor factorization techniques such as PARAFAC2 have been used for extracting meaningful medical concepts from EHRs, existing methods fail to capture non-linear and complex temporal patterns and struggle with missing entries. In this paper, we propose</i> REPAR<i>, an</i> R<i>NN R</i>E<i>gularized Robust</i> PAR<i>AFAC2 method to model complex temporal dependencies and enhance robustness in the presence of missing data. Our approach employs Recurrent Neural Networks (RNNs) for temporal regularization and a low-rank constraint for robustness, enabling precise patient subgroup identification and improved clinical decision-making in noisy EHR data. We design a hybrid optimization framework that handles multiple regularizations and various data types. REPAR is evaluated on 3 real-world EHR datasets, demonstrating improved reconstruction and robustness under missing data. Two case studies further showcase REPAR's ability to extract meaningful dynamic phenotypes and enhance phenotype predictability from noisy temporal EHRs.</i>