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

Formative Evaluation of Digital Health Interventions (2797) Depression and Mood Disorders; Suicide Prevention (1509) Web-based and Mobile Health Interventions (3616) Engagement with and Adherence to Digital Health Interventions, Law of Attrition (536) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1790)

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
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Neidi Neidi @Neidi94430664

Check out this fascinating article by @jmirpub! https://t.co/C0nV2eVS0Y

2:47 PM · Dec 30, 2024

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JMIR Publications JMIR Publications @jmirpub

RT @Neidi94430664: Check out this fascinating article by @jmirpub! https://t.co/C0nV2eVS0Y

3:30 PM · Dec 30, 2024

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