Unsupervised Learning of Disease Progression Models

Abstract

Chronic diseases, such as Alzheimer’s Disease, Diabetes, and Chronic Obstructive Pulmonary Disease, usually progress slowly over a long period of time, causing increasing burden to the patients, their families, and the healthcare system. A better understanding of their progression is instrumental in early diagnosis and personalized care. Modeling disease progression based on real-world evidence is a very challenging task due to the incompleteness and irregularity of the observations, as well as the heterogeneity of the patient conditions. In this paper, we propose a probabilistic disease progression model that address these challenges. As compared to existing disease progression models, the advantage of our model is three-fold: 1) it learns a continuous-time progression model from discrete-time observations with non-equal intervals; 2) it learns the full progression trajectory from a set of incomplete records that only cover short segments of the progression; 3) it learns a compact set of medical concepts as the bridge between the hidden progression process and the observed medical evidence, which are usually extremely sparse and noisy. We demonstrate the capabilities of our model by applying it to a real-world COPD patient cohort and deriving some interesting clinical insights.