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

Depression and Mood Disorders; Suicide Prevention (1333) Artificial Intelligence (1313) Chatbots and Conversational Agents (509) Generative Language Models Including ChatGPT (283) Machine Learning (1427) Natural Language Processing (674) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1473) Methods and New Tools in Mental Health Research (439)

Published on in Vol 11 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/57362, first published .
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

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 Tweetations Metrics

Salim Salmi   1 , MSc ;   Saskia Mérelle   1 , PhD ;   Renske Gilissen   1 , PhD ;   Rob van der Mei   2 , PhD ;   Sandjai Bhulai   3 , PhD

1 Research Department, 113 Suicide Prevention, Amsterdam, Netherlands

2 Department of Stochastics, Centrum Wiskunde & Informatica, Amsterdam, Netherlands

3 Department of Mathematics, Vrije Universiteit Amsterdam, Amsterdam, Netherlands

Corresponding Author:

  • Salim Salmi, MSc
  • Research Department
  • 113 Suicide Prevention
  • Paasheuvelweg 25
  • Amsterdam, 1105 BP
  • Netherlands
  • Phone: 31 640673474
  • Email: s.salmi@113.nl