MuLan: Multilevel Language-based Representation Learning for Disease Progression Modeling

Modeling patient disease progression using Electronic Health Records (EHRs) is crucial to assist clinical decision making. In recent years, deep learning models such as Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) have shown great success in handling sequential multivariate data, such as EHRs. Despite their great success, it is often difficult to interpret and visualize patient disease progression learned from these models in a meaningful yet unified way. In this work, we present MuLan: a Multilevel Language-based representation learning framework that can automatically learn a hierarchical representation for EHRs at entry, event, and visit levels. We validate MuLan on modeling the progression of an extremely challenging disease, septic shock, by using real-world EHRs. Our results showed that these unified multilevel representations can be utilized not only for interpreting and visualizing the latent mechanism of patients' septic shock progressions but also for early detection of septic shock.