Modeling Disease Progression Trajectories from Longitudinal Observational Data
This research provides insights into Type 1 Diabetes progression, which could inform prevention trials and personalized treatments for affected individuals.
The paper models disease progression patterns using Hidden Markov Models (HMM) on longitudinal observational data from the T1DI study group for Type 1 Diabetes (T1D). The method successfully discovers distinct disease progression trajectories that are consistent with recent published findings.
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.