This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (2491) mHealth for Data Collection and Research (981) Machine Learning (1501) Alcohol Abuse, Alcoholism Prevention and Recovery (303) Artificial Intelligence (1421) Decision Support for Health Professionals (1210) mHealth for Wellness, Behavior Change and Prevention (2922)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/39862, first published .
Leveraging Mobile Phone Sensors, Machine Learning, and Explainable Artificial Intelligence to Predict Imminent Same-Day Binge-drinking Events to Support Just-in-time Adaptive Interventions: Algorithm Development and Validation Study

Leveraging Mobile Phone Sensors, Machine Learning, and Explainable Artificial Intelligence to Predict Imminent Same-Day Binge-drinking Events to Support Just-in-time Adaptive Interventions: Algorithm Development and Validation Study

Leveraging Mobile Phone Sensors, Machine Learning, and Explainable Artificial Intelligence to Predict Imminent Same-Day Binge-drinking Events to Support Just-in-time Adaptive Interventions: Algorithm Development and Validation Study

Authors of this article:

Sang Won Bae1 Author Orcid Image ;   Brian Suffoletto2 Author Orcid Image ;   Tongze Zhang1 Author Orcid Image ;   Tammy Chung3 Author Orcid Image ;   Melik Ozolcer1 Author Orcid Image ;   Mohammad Rahul Islam1 Author Orcid Image ;   Anind K Dey4 Author Orcid Image
Article Authors Cited by (8) Tweetations (4) Metrics

Journals

  1. Wyant K, Moshontz H, Ward S, Fronk G, Curtin J. Acceptability of Personal Sensing Among People With Alcohol Use Disorder: Observational Study. JMIR mHealth and uHealth 2023;11:e41833 View
  2. Khan M, Chang S. Alcohol and the Brain–Gut Axis: The Involvement of Microglia and Enteric Glia in the Process of Neuro-Enteric Inflammation. Cells 2023;12(20):2475 View
  3. Sezgin E, McKay I. Behavioral health and generative AI: a perspective on future of therapies and patient care. npj Mental Health Research 2024;3(1) View
  4. Perski O, Kale D, Leppin C, Okpako T, Simons D, Goldstein S, Hekler E, Brown J, Or C. Supervised machine learning to predict smoking lapses from Ecological Momentary Assessments and sensor data: Implications for just-in-time adaptive intervention development. PLOS Digital Health 2024;3(8):e0000594 View
  5. Bae S, Chung T, Zhang T, Ozolcer M, Dey A, Islam M. Enhancing Interpretable, Transparent and Unobtrusive Detection of Acute Marijuana Intoxication in Natural Environments: Harnessing Smart Devices and Explainable AI to Empower Just-In-Time Adaptive Interventions (Preprint). JMIR AI 2023 View