Predicting suicidal thoughts and behavior among adolescents using the risk and protective factor framework: A large-scale machine learning approach.

Journal: PloS one

PMID: 34731169

Abstract

INTRODUCTION: Addressing the problem of suicidal thoughts and behavior (STB) in adolescents requires understanding the associated risk factors. While previous research has identified individual risk and protective factors associated with many adolescent social morbidities, modern machine learning approaches can help identify risk and protective factors that interact (group) to provide predictive power for STB. This study aims to develop a prediction algorithm for STB among adolescents using the risk and protective factor framework and social determinants of health.

Authors

Orion Weller

Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, United States of America.
Luke Sagers

Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02215, USA.
Carl Hanson

Department of Public Health, Brigham Young University, Provo, Utah, United States of America.
Michael Barnes

Department of Public Health, Brigham Young University, Provo, Utah, United States of America.
Quinn Snell

Department of Computer Science, Brigham Young University, Provo, Utah, United States of America.
E Shannon Tass

Department of Statistics, Brigham Young University, Provo, Utah, United States of America.

Keywords

Adolescent Bullying Cannabis Female Forecasting Humans Internet Machine Learning Male Risk Factors Schools Students Suicidal Ideation Suicide Suicide Prevention Suicide, Attempted Surveys and Questionnaires Utah Young Adult

External Resources

View on PubMed Access via DOI PubMed (34731169)

Predicting suicidal thoughts and behavior among adolescents using the risk and protective factor framework: A large-scale machine learning approach.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals