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

Depression and Mood Disorders; Suicide Prevention (1802) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (2369) Natural Language Processing (997) Methods and New Tools in Mental Health Research (615) Machine Learning (2316) Chatbots and Conversational Agents (809) Artificial Intelligence (2469) Generative Language Models Including ChatGPT (894)

Published on in Vol 11 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/57362, first published .
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The Most Effective Interventions for Classification Model Development to Predict Chat Outcomes Based on the Conversation Content in Online Suicide Prevention Chats: Machine Learning Approach

The Most Effective Interventions for Classification Model Development to Predict Chat Outcomes Based on the Conversation Content in Online Suicide Prevention Chats: Machine Learning Approach

Authors of this article:

Salim Salmi1 Author Orcid Image ;   Saskia Mérelle1 Author Orcid Image ;   Renske Gilissen1 Author Orcid Image ;   Rob van der Mei2 Author Orcid Image ;   Sandjai Bhulai3 Author Orcid Image
Article Authors Cited by (4) Tweetations Metrics

Journals

  1. Côté L, Mishara B. Effects of the Canadian Suicide Prevention Service's Text Interventions on Texters' Emotions, Distress Relief, Perceived Abilities, and Practices Associated With Better Outcomes. Suicide and Life-Threatening Behavior 2025;55(2) View
  2. Mokheleli T, Makaba T, Ndayizigamiye P, Ndlovu N, Twinomurinzi H. Artificial intelligence in suicide risk assessment: a systematic literature review. Discover Artificial Intelligence 2026;6(1) View
  3. ten Thij M, Mérelle S, Gilissen R, Bollen J. Prevalence of Cognitive Distortion Markers in a Suicide Prevention Chat Service: Mixed Methods Study. JMIR Mental Health 2026;13:e81213 View
  4. Côté L, Mishara B, Blondin R. Youth Textline Study: Suicide Risk Detection and Beneficial Counseling Practices. Canadian Journal of Community Mental Health 2026:1 View