Disease progression models are important computational tools in healthcare and are used for tasks such as improving disease understanding, informing drug discovery, and aiding in patient management. Although many algorithms for time series modeling exist, healthcare applications face particular challenges such as small datasets, medication effects, disease heterogeneity, and a desire for personalized predictions. In this work, we present a disease progression model that addresses these needs by proposing a probabilistic time-series model that captures individualized disease states, personalized medication effects, disease-state medication effects, or any combination thereof. The model builds on the framework of an input-output hidden Markov model where the parameters are learned using a structured variational approximation. To demonstrate the utility of the algorithm, we apply it to both synthetic and real-world datasets. In the synthetic case, we demonstrate the benefits afforded by the proposed model as compared to standard techniques. In the real-world cases, we use two Parkinson’s disease datasets to show improved predictive performance when ground truth is available and clinically relevant insights that are not revealed via classic Markov models when ground truth is not available.