Capturing Feature-Level Irregularity in Disease Progression Modeling

Abstract

Disease progression modeling (DPM) analyzes patients' electronic medical records (EMR) to predict the health state of patients, which facilitates accurate prognosis, early detection and treatment of chronic diseases. However, EMR are irregular because patients visit hospital irregularly based on the need of treatment. For each visit, they are typically given different diagnoses, prescribed various medications and lab tests. Consequently, EMR exhibit irregularity at the feature level. To handle this issue, we propose a model based on the Gated Recurrent Unit by decaying the effect of previous records using fine-grained feature-level time span information, and learn the decaying parameters for different features to take into account their different behaviours like decaying speeds under irregularity. Extensive experimental results in both an Alzheimer's disease dataset and a chronic kidney disease dataset demonstrate that our proposed model of capturing feature-level irregularity can effectively improve the accuracy of DPM.