• Corpus ID: 228083978

Modeling Disease Progression Trajectories from Longitudinal Observational Data

@article{Kwon2020ModelingDP,
  title={Modeling Disease Progression Trajectories from Longitudinal Observational Data},
  author={Bum Chul Kwon and Peter Achenbach and Jessica L. Dunne and William A. Hagopian and Markus Lundgren and Kenney Ng and Riitta Veijola and Brigitte I. Frohnert and Vibha Anand and the T1DI Study Group},
  journal={AMIA ... Annual Symposium proceedings. AMIA Symposium},
  year={2020},
  volume={2020},
  pages={
          668-676
        }
}
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… 

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