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• W2913263577 endingPage "e12561" @default.
• W2913263577 startingPage "e12561" @default.
• W2913263577 abstract "Background: Medication nonadherence can compound into severe medical problems for patients. Identifying patients who are likely to become nonadherent may help reduce these problems. Data-driven machine learning models can predict medication adherence by using selected indicators from patients’ past health records. Sources of data for these models traditionally fall under two main categories: (1) proprietary data from insurance claims, pharmacy prescriptions, or electronic medical records and (2) survey data collected from representative groups of patients. Models developed using these data sources often are limited because they are proprietary, subject to high cost, have limited scalability, or lack timely accessibility. These limitations suggest that social health forums might be an alternate source of data for adherence prediction. Indeed, these data are accessible, affordable, timely, and available at scale. However, they can be inaccurate." @default.
• W2913263577 created "2019-02-21" @default.
• W2913263577 creator A5035288182 @default.
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• W2913263577 date "2019-04-04" @default.
• W2913263577 modified "2023-09-25" @default.
• W2913263577 title "Medication Adherence Prediction Through Online Social Forums: A Case Study of Fibromyalgia" @default.
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• W2913263577 doi "https://doi.org/10.2196/12561" @default.
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