DiseaseProgressionModeling-HMM

Code to implement a personalized input output hidden Markov model (PIOHMM) and other hidden Markov model variations. PIOHMMs are described in K.A. Severson, L.M. Chahine, L. Smolensky, K. Ng, J. Hu and S. Ghosh, 'Personalized Input-Output Hidden Markov Models for Disease Progression Modeling' MLHC 2020. Full details are available here. The PIOHMM model class is in piohmm.py.

Running the code

See the jupyter notebook 'Sample Model' for a simple example of the model. There are three primary components for using a PIOHMM:

• HMM to specify the particular model; see __init__ for a description of the options
• learn_model to perform inference
• predict_sequence to use the Viterbi algorithm to make state predictions