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

Formative Evaluation of Digital Health Interventions (3229) Depression and Mood Disorders; Suicide Prevention (1691) Web-based and Mobile Health Interventions (4018) Engagement with and Adherence to Digital Health Interventions, Law of Attrition (638) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (2111)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/53768, first published .
Use of Random Forest to Predict Adherence in an Online Intervention for Depression Using Baseline and Early Usage Data: Model Development and Validation on Retrospective Routine Care Log Data

Use of Random Forest to Predict Adherence in an Online Intervention for Depression Using Baseline and Early Usage Data: Model Development and Validation on Retrospective Routine Care Log Data

Use of Random Forest to Predict Adherence in an Online Intervention for Depression Using Baseline and Early Usage Data: Model Development and Validation on Retrospective Routine Care Log Data

Authors of this article:

Franziska Wenger1 Author Orcid Image ;   Caroline Allenhof2 Author Orcid Image ;   Simon Schreynemackers3 Author Orcid Image ;   Ulrich Hegerl1, 3 Author Orcid Image ;   Hanna Reich1, 3 Author Orcid Image
Article Authors Cited by (3) Tweetations (2) Metrics

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  2. Yeneakal K, Teferi G, Mihret T, Mengistu A, Tizie S, Tadele M. Predicting antiretroviral therapy adherence status of adult HIV-positive patients using machine-learning Northwest, Ethiopia, 2025. BMC Medical Informatics and Decision Making 2025;25(1) View
  3. Faye L, Hosu M, Dlatu N, Iruedo J, Apalata T. Predicting treatment adherence in patients with drug-resistant tuberculosis: insights from socioeconomic, demographic, and clinical factors of patients in the rural Eastern Cape. Frontiers in Tuberculosis 2025;3 View