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

Natural Language Processing (692) Clinical Information and Decision Making (1407) Machine Learning (1465) Depression and Mood Disorders; Suicide Prevention (1363) Diagnostic Tools in Mental Health (242) Intentional Self-Harm (96) Clinical Information and Decision Making (1407) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1514) Machine Learning (1465) Depression and Mood Disorders; Suicide Prevention (1363) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1514) Intentional Self-Harm (96) Diagnostic Tools in Mental Health (242)

Published on in Vol 11 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/50221, first published .
Detection of Suicidal Ideation in Clinical Interviews for Depression Using Natural Language Processing and Machine Learning: Cross-Sectional Study

Detection of Suicidal Ideation in Clinical Interviews for Depression Using Natural Language Processing and Machine Learning: Cross-Sectional Study

Detection of Suicidal Ideation in Clinical Interviews for Depression Using Natural Language Processing and Machine Learning: Cross-Sectional Study

Authors of this article:

Tim M H Li1 Author Orcid Image ;   Jie Chen1 Author Orcid Image ;   Framenia O C Law1 Author Orcid Image ;   Chun-Tung Li1 Author Orcid Image ;   Ngan Yin Chan1 Author Orcid Image ;   Joey W Y Chan1 Author Orcid Image ;   Steven W H Chau1 Author Orcid Image ;   Yaping Liu1 Author Orcid Image ;   Shirley Xin Li2, 3 Author Orcid Image ;   Jihui Zhang1, 4 Author Orcid Image ;   Kwong-Sak Leung5, 6 Author Orcid Image ;   Yun-Kwok Wing1 Author Orcid Image
Article Authors Cited by (4) Tweetations (2) Metrics

Journals

  1. Huang R, Yi S, Chen J, Chan K, Chan J, Chan N, Li S, Wing Y, Li T. Exploring the Role of First-Person Singular Pronouns in Detecting Suicidal Ideation: A Machine Learning Analysis of Clinical Transcripts. Behavioral Sciences 2024;14(3):225 View
  2. Zhang Y, Folarin A, Dineley J, Conde P, de Angel V, Sun S, Ranjan Y, Rashid Z, Stewart C, Laiou P, Sankesara H, Qian L, Matcham F, White K, Oetzmann C, Lamers F, Siddi S, Simblett S, Schuller B, Vairavan S, Wykes T, Haro J, Penninx B, Narayan V, Hotopf M, Dobson R, Cummins N. Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model. Journal of Affective Disorders 2024;355:40 View
  3. Kazi Golam Rabbany , Aisultan Shoiynbek , Darkhan Kuanyshbay , Assylbek Mukhametzhanov , Akbayan Bekarystankyzy , Temirlan Shoiynbek . A REVIEW ON MACHINE LEARNING APPROACHES FOR THE DETECTION OF SUICIDAL TENDENCIES. World Science 2024;(3(85)) View
  4. Oliveira F, Neri D, Ribeiro K, Oliveira L, Santos E, Pereira M, Lima S. O USO DA INTELIGÊNCIA ARTIFICIAL NA IDENTIFICAÇÃO DO PACIENTE PSIQUIÁTRICO COM TENDÊNCIA SUICIDA. Revista Contemporânea 2024;4(10):e6253 View