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

Depression and Mood Disorders; Suicide Prevention (1502) Artificial Intelligence (1684) Chatbots and Conversational Agents (619) Generative Language Models Including ChatGPT (473) Machine Learning (1699) Natural Language Processing (791) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1775) Methods and New Tools in Mental Health Research (497)

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 (1) 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