Stage-Aware Event-Based Modeling (SA-EBM) for Disease Progression

Abstract

As diseases progress, the number of cognitive and biological biomarkers they impact increases. By formulating probabilistic models with this basic assumption, Event-Based Models (EBMs) enable researchers to discover the progression of a disease that makes earlier diagnosis and effective clinical interventions possible. We build on prior EBMs with two major improvements: (1) dynamic estimation of healthy and pathological biomarker distributions, and (2) explicit modeling of the distribution of disease stages. We tested existing approaches and our novel approach on a benchmark of 9,000 synthetic datasets, inspired from real-world data. We found that our stage-aware EBM (SA-EBM) significantly outperforms prior methods, such as Gaussian Mixture Model (GMM) EBM, Kernel Density Estimation EBM and Discriminative EBM, on ordering and staging tasks.