Accepted for/Published in: JMIR Biomedical Engineering
Date Submitted: Apr 9, 2021
Date Accepted: Jan 20, 2022
Reducing Treatment Burden Among People with Chronic Conditions Using Machine Learning
ABSTRACT
Predictive algorithms can help people living with chronic conditions make lifestyle decisions that fit with their physiology and personality as well as adapt to their changing contexts. We describe the use of two families of machine learning models to address these opportunities. Outcomes models forecast health changes over several months. Determining which factors most improve health predictions can indicate which lifestyle modifications are most likely to be beneficial. Adaptive support models predict the magnitude, timing, and content of behavioral targets, prompts, and reinforcements most likely to be effective for an individual. Together, these two types of models account for people’s physiology, capability, personality, and context, and deliver personalized interventions, promote engagement and most importantly, sustainable behavior change. This can help ease some of the challenges associated with lifestyle modification while managing a chronic condition, potentially leading to improvements in both clinical health outcomes and quality of life.
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