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
All Time | Last Month | Last Six Months | Last Year
There are currently no tweets available for this article within the specified range.