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

Formative Evaluation of Digital Health Interventions (2407) mHealth for Data Collection and Research (954) Machine Learning (1459) Alcohol Abuse, Alcoholism Prevention and Recovery (300) Artificial Intelligence (1363) Decision Support for Health Professionals (1181) mHealth for Wellness, Behavior Change and Prevention (2867)

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

Sang Won Bae   1 , PhD ;   Brian Suffoletto   2 , MD ;   Tongze Zhang   1 , MSc ;   Tammy Chung   3 , PhD ;   Melik Ozolcer   1 , BE ;   Mohammad Rahul Islam   1 , BSc ;   Anind K Dey   4 , PhD

1 Human-Computer Interaction and Human-Centered AI Systems Lab, AI for Healthcare Lab, School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, United States

2 Department of Emergency Medicine, Stanford University, Stanford, CA, United States

3 Institute for Health, Healthcare Policy and Aging Research, Rutgers University, Newark, NJ, United States

4 Information School, University of Washington, Seattle, WA, United States

Corresponding Author:

  • Sang Won Bae, PhD
  • Human-Computer Interaction and Human-Centered AI Systems Lab
  • AI for Healthcare Lab, School of Systems and Enterprises
  • Stevens Institute of Technology
  • 1 Castle Point Terrace
  • Hoboken, NJ, 07030
  • United States
  • Phone: 1 4122658616
  • Email: sbae4@stevens.edu