Predicting Therapy Adherence : A Machine Learning Approach

Thumbnail Image

Persistent link to this item

View Statistics

Journal Title

Journal ISSN

Volume Title


Predicting Therapy Adherence : A Machine Learning Approach

Published Date




Thesis or Dissertation


Ensuring adherence to medical therapy has been an open problem in health care practices since the Hippocrates Oath (400 BC) to modern medicine. In an ideal world, people would follow their doctor's recommendations. They would stick to their diet to lose weight, take their medication on time, and use their electronic health devices as recommended by the doctors. But the planned routine is rarely followed, causing a financial burden in the order of billions of dollars for the national healthcare system and many billions of dollars worldwide. A key mechanism to revert a tendency of non-adherence is early personalized intervention, targeting the key factors of undesired behavior, but this task is not trivial. After starting their therapy, individuals have an unpredictable series of life events that may impact their willingness to keep with the necessary therapy routine. Only recently, we achieved the ability to passively collect individual-level therapy data as patients progress in their treatments using digital devices. In this thesis, we proposes various machine-learning strategies that aim to leverage the data collected at the early stages of medical therapies to predict future adherence and recommend early accurate interventions that align with each individual's desired outcomes. We narrow most of the analysis in two sleep apnea therapies, Continuous Positive Airway Pressure (CPAP) and Upper-Airway Stimulation (UAS). But to reinforce the generalization of our methods we also show how they can be applied for the growth-hormone therapy management.


University of Minnesota Ph.D. dissertation. 2021. Major: Computer Science. Advisor: Jaideep Srivastava. 1 computer file (PDF); 148 pages.

Related to




Series/Report Number

Funding information

Isbn identifier

Doi identifier

Previously Published Citation

Suggested citation

Lima Diniz Araujo, Matheus. (2021). Predicting Therapy Adherence : A Machine Learning Approach. Retrieved from the University Digital Conservancy,

Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.