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A new approach to integrating patient-generated data with expert knowledge for personalized goal setting: A pilot study.

, , , , , , , , , , and . Int. J. Medical Informatics, (2020)

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A new approach to integrating patient-generated data with expert knowledge for personalized goal setting: A pilot study., , , , , , , , , and 1 other author(s). Int. J. Medical Informatics, (2020)The use of social media in nutrition interventions for adolescents and young adults - A systematic review., , and . Int. J. Medical Informatics, (2018)Who needs what (features) when? Personalizing engagement with data-driven self-management to improve health equity., , , , , , , , , and . J. Biomed. Informatics, (August 2023)An Intelligent Voice Assistant for Diabetes Self-Management: T2D2 - Taming Type 2 Diabetes, Together., , , , , , , and . AMIA, AMIA, (2018)Feasibility of a machine learning based method to generate personalized nutrition goals for diabetes self-management., , , , , , , , and . AMIA, AMIA, (2019)The Role of Explanations in Casual Observational Learning about Nutrition., , , and . CHI, page 4097-4145. ACM, (2017)A visual analytics approach for pattern-recognition in patient-generated data., , , , , , and . J. Am. Medical Informatics Assoc., 25 (10): 1366-1374 (2018)Adapting the stage-based model of personal informatics for low-resource communities in the context of type 2 diabetes., , , , and . J. Biomed. Informatics, (2020)Designing for engagement with self-monitoring: A user-centered approach with low-income, Latino adults with Type 2 Diabetes., , , , and . Int. J. Medical Informatics, (2019)From Reflection to Action: Combining Machine Learning with Expert Knowledge for Nutrition Goal Recommendations., , , , , , , , , and 3 other author(s). CHI, page 206:1-206:17. ACM, (2021)