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

Clinical Mental Health Informatics (38) Depression and Mood Disorders; Suicide Prevention (1363) Intentional Self-Harm (96) Mental Health Issues in Adolescence (442) Pediatrics (137) Digital Biomarkers and Digital Phenotyping (191) Machine Learning (1465)

Published on in Vol 10 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/47084, first published .
Assessing Detection of Children With Suicide-Related Emergencies: Evaluation and Development of Computable Phenotyping Approaches

Assessing Detection of Children With Suicide-Related Emergencies: Evaluation and Development of Computable Phenotyping Approaches

Assessing Detection of Children With Suicide-Related Emergencies: Evaluation and Development of Computable Phenotyping Approaches

Authors of this article:

Juliet Beni Edgcomb1, 2 Author Orcid Image ;   Chi-hong Tseng3 Author Orcid Image ;   Mengtong Pan3 Author Orcid Image ;   Alexandra Klomhaus3 Author Orcid Image ;   Bonnie T Zima1, 2 Author Orcid Image
Article Authors Cited by (4) Tweetations (6) Metrics

Journals

  1. Tio E, Misztal M, Felsky D. Evidence for the biopsychosocial model of suicide: a review of whole person modeling studies using machine learning. Frontiers in Psychiatry 2024;14 View
  2. Zima B, Edgcomb J, Fortuna L. Identifying Precise Targets to Improve Child Mental Health Care Equity. Child and Adolescent Psychiatric Clinics of North America 2024;33(3):471 View
  3. Atmakuru A, Shahini A, Chakraborty S, Seoni S, Salvi M, Hafeez-Baig A, Rashid S, Tan R, Barua P, Molinari F, Acharya U. Artificial intelligence-based suicide prevention and prediction: A systematic review (2019–2023). Information Fusion 2025;114:102673 View
  4. Edgcomb J, Olde Loohuis L, Tseng C, Klomhaus A, Choi K, Ponce C, Zima B. Electronic Health Record Phenotyping of Pediatric Suicide-Related Emergency Department Visits. JAMA Network Open 2024;7(10):e2442091 View