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Classification of Suicide Attempts through a Machine Learning Algorithm Based on Multiple Systemic Psychiatric

Jihoon Oh¹, Kyongsik Yun^2,3, Ji-Hyun Hwang¹

¹Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea.

Frontiers in Psychiatry

|October 18, 2017

Summary

This summary is machine-generated.

This study shows that machine learning models using clinical scales can accurately predict suicide attempts in patients with depression and anxiety. The Emotion Regulation Questionnaire was a key predictor, aiding clinicians in identifying at-risk individuals.

Keywords:

Psychiatric Status Rating Scales anxiety disorders depression machine learning suicide

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

Psychiatry
Machine Learning
Clinical Psychology

Background:

Suicide prevention is a critical public health concern.
Accurate identification of individuals at high risk for suicide attempts is essential.
Existing clinical assessments may not fully capture the complexity of suicide risk.

Purpose of the Study:

To determine if data from multiple clinical scales can classify suicide attempts.
To develop and evaluate a machine learning model for suicide attempt prediction.
To identify key predictors of suicide attempts in a clinical population.

Main Methods:

An artificial neural network classifier was trained using 41 variables (31 clinical scales, 10 sociodemographic factors).
The study included 573 patients diagnosed with depression and anxiety disorders.
Variable importance was ranked, and model performance was assessed using accuracy and Area Under the Receiver Operating Characteristic Curve (AUROC).

Main Results:

The model achieved high accuracy in detecting suicide attempts (93.7% for 1-month, 90.8% for 1-year, 87.4% for lifetime).
AUROC values were strong, particularly for 1-month detection (0.93).
The Emotion Regulation Questionnaire was the most significant predictor; performance remained robust even with the top five predictors.

Conclusions:

Self-report clinical scales provide valuable information for classifying suicide attempts.
A machine learning approach utilizing these scales can effectively identify high-risk patients.
This method offers a promising tool for clinicians to enhance suicide risk assessment in clinical settings.