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

Proposals (eHealth) (80) Substance Abuse (426) Artificial Intelligence (1407) Mobile Health (mhealth) (2972) Machine Learning (1488) Machine Learning (53)

Published on in Vol 13 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/46493, first published .
Personalized Deep Learning for Substance Use in Hawaii: Protocol for a Passive Sensing and Ecological Momentary Assessment Study

Personalized Deep Learning for Substance Use in Hawaii: Protocol for a Passive Sensing and Ecological Momentary Assessment Study

Personalized Deep Learning for Substance Use in Hawaii: Protocol for a Passive Sensing and Ecological Momentary Assessment Study

Authors of this article:

Yinan Sun1 Author Orcid Image ;   Ali Kargarandehkordi1 Author Orcid Image ;   Christopher Slade1 Author Orcid Image ;   Aditi Jaiswal1 Author Orcid Image ;   Gerald Busch2 Author Orcid Image ;   Anthony Guerrero2 Author Orcid Image ;   Kristina T Phillips3 Author Orcid Image ;   Peter Washington1 Author Orcid Image
Article Authors Cited by (2) Tweetations (2) Metrics

Journals

  1. Slade C, Benzo R, Washington P. Design Guidelines for Improving Mobile Sensing Data Collection: Prospective Mixed Methods Study. Journal of Medical Internet Research 2024;26:e55694 View
  2. Li S, Fan C, Kargarandehkordi A, Sun Y, Slade C, Jaiswal A, Benzo R, Phillips K, Washington P. Monitoring Substance Use with Fitbit Biosignals: A Case Study on Training Deep Learning Models Using Ecological Momentary Assessments and Passive Sensing. AI 2024;5(4):2725 View