Paper Type

Complete

Abstract

This research presents an umbrella review that mainly focuses on the application of machine learning in the prediction and prevention of suicide, identifying research gaps and proposing future research directions for this field. By accessing recent studies, this review identifies the strengths, limitations, and ethical considerations of utilizing machine learning to predict suicidal thoughts. Key findings indicate that machine learning has the potential to increase intervention efficacy and prediction accuracy, but they also need for rigorous validation, ethical transparency, and integration of clinical expertise. In addition, the review promotes a multidisciplinary approach to integrate machine learning techniques with real-world clinical applications. Future directions are suggested to focus on personalized intervention strategies, improving data interoperability, and exploring novel data sources to refine and expand the use of machine learning in this area of mental health.

Paper Number

1658

Author Connect URL

https://authorconnect.aisnet.org/conferences/AMCIS2024/papers/1658

Comments

SIGHEALTH

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Aug 16th, 12:00 AM

An Umbrella Review for Machine Learning Suicide Prediction and Prevention in Mental Health

This research presents an umbrella review that mainly focuses on the application of machine learning in the prediction and prevention of suicide, identifying research gaps and proposing future research directions for this field. By accessing recent studies, this review identifies the strengths, limitations, and ethical considerations of utilizing machine learning to predict suicidal thoughts. Key findings indicate that machine learning has the potential to increase intervention efficacy and prediction accuracy, but they also need for rigorous validation, ethical transparency, and integration of clinical expertise. In addition, the review promotes a multidisciplinary approach to integrate machine learning techniques with real-world clinical applications. Future directions are suggested to focus on personalized intervention strategies, improving data interoperability, and exploring novel data sources to refine and expand the use of machine learning in this area of mental health.

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