Despite progress on many fronts in the management of chronic diseases, there are still many open questions in the field. A better understanding of chronic disease could enable improved patient care management, and faster, more efficient drug development as a result of better designed clinical trials. IBM Research is using machine learning as a tool in the pursuit of revealing the complexities of these diseases. At this week’s Machine Learning for Healthcare Conference, we present our progress in this area as motivated by Parkinson’s disease (PD).

PD is a chronic, progressive neurodegenerative disorder with heterogeneous symptoms which may affect both motor and non-motor function. PD is one of the top ten leading causes of death in persons over 65 years of age in the United States. It is estimated that 6 million people worldwide and 1 million people in the United States live with PD, and this prevalence is expected to double by 2040 – making the need for research and a better understanding of the disease even more urgent.

In collaboration with The Michael J. Fox Foundation for Parkinson’s Research, our team of researchers at IBM is aiming to develop improved disease progression models that can help clinicians understand how the disease progresses in relation to the emergence of symptoms, even when those patients are taking symptom-modifying medications.

Our proposed approach addresses the needs of learning disease states while modeling medication effects, which can be a function of disease state and/or personalized response. It is unique in its focus on modeling data from patients who are on medications. The approach builds on the framework of a hidden Markov model and uses variational inference to learn personalized effects. After learning the model, insights can be derived both from interpreting the parameters of the model to learn more about the disease, as well as analyzing predictions for a particular cohort of patients.

The proposed model and learning algorithm will be presented at the 2020 Conference for Machine Learning for Healthcare. Although the work was motivated by PD, we hope it might be useful or inspire similar work and exploration in other chronic conditions such as diabetes, Alzheimer’s disease, and ALS. Developing the tools for analysis is only the first step in the collaboration with The Michael J. Fox Foundation. Our next results will focus on the clinical insights we have derived by applying these models to the extensive amounts of data collected by The Michael J. Fox Foundation’s landmark clinical study, the Parkinson’s Progression Markers Initiative.


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