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

Formative Evaluation of Digital Health Interventions (2491) Depression and Mood Disorders; Suicide Prevention (1392) Web-based and Mobile Health Interventions (3357) Engagement with and Adherence to Digital Health Interventions, Law of Attrition (505) Machine Learning (55) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1565)

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 Tweetations Metrics

Franziska Wenger   1 ;   Caroline Allenhof   2 , Dr rer nat ;   Simon Schreynemackers   3 , MSc ;   Ulrich Hegerl   1, 3 , Prof Dr Med ;   Hanna Reich   1, 3 , Dr rer nat

1 Clinic for Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe University Frankfurt, Frankfurt am Main, Germany

2 Depression Research Centre, German Depression Foundation, Leipzig, Germany

3 Depression Research Centre, German Depression Foundation, Frankfurt, Germany

Corresponding Author: