Objectives Patient representation learning refers to learning a dense mathematical
representation of a patient that encodes meaningful information from Electronic Health …

Objective Temporal electronic health records (EHRs) contain a wealth of information for
secondary uses, such as clinical events prediction and chronic disease management …

While recent developments of deep learning models have led to record-breaking
achievements in many areas, the lack of sufficient interpretation remains a problem for many …

Due to the insufficiency of electronic health records (EHR) data utilized in practical diagnosis
prediction scenarios, most works are devoted to learning powerful patient representations …

Deep learning techniques are capable of capturing complex input-output relationships, and
have been widely applied to the diagnosis prediction task based on web-based patient …

Patients' readmission can be considered as a critical factor affecting cost reduction while
maintaining a high-quality treatment of patients. Therefore, predicting and controlling …

Hospital readmission is among the most critical issues in the healthcare system due to its
high prevalence and cost. The improvement effort necessitates reliable prediction models …

Abstract Non-negative Tensor Factorization (NTF) has been shown effective to discover
clinically relevant and interpretable phenotypes from Electronic Health Records (EHR) …

Contextual behavior modeling uses data from multiple contexts to discover patterns for
predictive analysis. However, existing behavior prediction models often face difficulties …

Computational phenotyping allows for unsupervised discovery of subgroups of patients as
well as corresponding co‐occurring medical conditions from electronic health records …