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

Artificial Intelligence (1194) Machine Learning (1383) Ecological Momentary Assessment (EMA) (294) Users' and Patients' Needs for Mental Health Services (381) mHealth for Data Collection and Research (912) mHealth for Wellness, Behavior Change and Prevention (2739) Diabetes Self-Management (463) Apps, Mobile, Wearables for Diabetes (288) Web-based and Mobile Health Interventions (3113) Clinical Information and Decision Making (1328) Mobile Health (mhealth) (2760)

Published on in Vol 23, No 8 (2021): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27709, first published .
A Machine Learning Approach to Passively Informed Prediction of Mental Health Risk in People with Diabetes: Retrospective Case-Control Analysis

A Machine Learning Approach to Passively Informed Prediction of Mental Health Risk in People with Diabetes: Retrospective Case-Control Analysis

A Machine Learning Approach to Passively Informed Prediction of Mental Health Risk in People with Diabetes: Retrospective Case-Control Analysis

Authors of this article:

Jessica Yu1 Author Orcid Image ;   Carter Chiu1 Author Orcid Image ;   Yajuan Wang1 Author Orcid Image ;   Eldin Dzubur1 Author Orcid Image ;   Wei Lu1 Author Orcid Image ;   Julia Hoffman1 Author Orcid Image
Article Authors Cited by (2) Tweetations (3) Metrics

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  1. Kuhathasan N, Ballester P, Minuzzi L, MacKillop J, Frey B. Predictors of perceived symptom change with acute cannabis use for mental health conditions in a naturalistic sample: A machine learning approach. Comprehensive Psychiatry 2023;122:152377 View
  2. Fernandes G, Choi A, Schauer J, Pfammatter A, Spring B, Darwiche A, Alshurafa N. An Explainable Artificial Intelligence Software Tool for Weight Management Experts (PRIMO): Mixed Methods Study. Journal of Medical Internet Research 2023;25:e42047 View