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Related Experiment Video

Updated: Jul 2, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

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Published on: May 15, 2020

Predicting suicidal behavior: a machine learning model.

A-M Vejnović1, V Tatalović, M Vujović

  • 1Department of Psychiatry and Psychological Medicine, Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia. ana-marija.vejnovic@mf.uns.ac.rs.

European Review for Medical and Pharmacological Sciences
|December 4, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning models can predict suicide attempts by analyzing patient data. A k-Nearest Neighbors (kNN) model achieved 87% accuracy, aiding in early risk identification and suicide prevention efforts.

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Last Updated: Jul 2, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Area of Science:

  • Psychiatry
  • Computational Medicine
  • Public Health

Background:

  • Timely identification of suicide risk factors is crucial for effective intervention and prevention.
  • Machine learning (ML) offers a powerful approach to analyze clinical data for developing predictive models.
  • Classifying patients into high- and low-risk groups can significantly aid in suicide prevention strategies.

Purpose of the Study:

  • To develop and evaluate a machine learning model for categorizing patients based on their risk of suicide attempts.
  • To analyze the influence of 18 observed features on the development of suicidal behavior using ML methods.
  • To create a predictive tool for classifying individuals into high- and low-risk categories for suicide attempts.

Main Methods:

  • Analysis of clinical data from 301 hospitalized psychiatric patients, divided into suicidal behavior and non-suicidal groups.
  • Application of machine learning methods to identify key features influencing suicidal behavior.
  • Development and training of a predictive model using the k-Nearest Neighbors (kNN) algorithm.

Main Results:

  • The kNN-based model demonstrated a classification accuracy of 87% for predicting suicide risk.
  • The model achieved a sensitivity of 87%, precision of 90%, and an F-score of 85% on the tested sample.
  • Identified key features influencing suicidal behavior through ML analysis.

Conclusions:

  • Early identification of suicidal behavior risk factors is paramount for effective suicide prevention.
  • Machine learning classification models can serve as valuable clinical tools for assessing suicide risk.
  • Expanding the dataset can enhance the classifier's performance and facilitate its integration into clinical practice for reducing suicide rates.