Machine Learning and Medication Adherence: Scoping Review

作者：Aaron Bohlmann, Javed Mostafa, Manish Kumar · 发表于：JMIRx Med · 年份：2021 · DOI：10.2196/26993 · 被引用次数：68 · 研究领域：Medication Adherence and Compliance、Mobile Health and mHealth Applications、Phosphodiesterase function and regulation

BACKGROUND: This is the first scoping review to focus broadly on the topics of machine learning and medication adherence. OBJECTIVE: This review aims to categorize, summarize, and analyze literature focused on using machine learning for actions related to medication adherence. METHODS: PubMed, Scopus, ACM Digital Library, IEEE, and Web of Science were searched to find works that meet the inclusion criteria. After full-text review, 43 works were included in the final analysis. Information of interest was systematically charted before inclusion in the final draft. Studies were placed into natural categories for additional analysis dependent upon the combination of actions related to medication adherence. The protocol for this scoping review was created using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. RESULTS: Publications focused on predicting medication adherence have uncovered 20 strong predictors that were significant in two or more studies. A total of 13 studies that predicted medication adherence used either self-reported questionnaires or pharmacy claims data to determine medication adherence status. In addition, 13 studies that predicted medication adherence did so using either logistic regression, artificial neural networks, random forest, or support vector machines. Of the 15 studies that predicted medication adherence, 6 reported predictor accuracy, the lowest of which was 77.6%. Of 13 ...