Modeling Disease Progression Trajectories from Longitudinal Observational Data
Abstract
Analyzing disease progression patterns can provide useful insights into the disease processes of many chronic conditions. These analyses may help inform recruitment for prevention trials or the development and personalization of treatments for those affected. We learn disease progression patterns using Hidden Markov Models (HMM) and distill them into distinct trajectories using visualization methods. We apply it to the domain of Type 1 Diabetes (T1D) using large longitudinal observational data from the T1DI study group. Our method discovers distinct disease progression trajectories that corroborate with recently published findings. In this paper, we describe the iterative process of developing the model. These methods may also be applied to other chronic conditions that evolve over time.
Keywords
Cite
@article{arxiv.2012.05324,
title = {Modeling Disease Progression Trajectories from Longitudinal Observational Data},
author = {Bum Chul Kwon and Peter Achenbach and Jessica L. Dunne and William Hagopian and Markus Lundgren and Kenney Ng and Riitta Veijola and Brigitte I. Frohnert and Vibha Anand and the T1DI Study Group},
journal= {arXiv preprint arXiv:2012.05324},
year = {2020}
}
Comments
9 pages, 5 figures, to be published in proceedings of AMIA Annual Symposium 2020