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Suicidal behaviour prediction models using machine learning techniques: A systematic review.

Noratikah Nordin1, Zurinahni Zainol1, Mohd Halim Mohd Noor1

  • 1School of Computer Sciences, Universiti Sains Malaysia, 11800 USM, Pulau Pinang, Malaysia.

Artificial Intelligence in Medicine
|October 7, 2022
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) models show promise for predicting suicidal behavior, with ensemble methods outperforming single models. Further research is needed to address challenges and advance suicide prediction capabilities.

Keywords:
Data miningMachine learningPrediction modelSuicidal behaviour

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Area of Science:

  • Artificial Intelligence
  • Psychiatry
  • Data Science

Background:

  • Early detection and prediction of suicidal behavior are critical for suicide prevention efforts.
  • Advances in artificial intelligence (AI) are driving research into machine learning (ML) applications for suicide risk assessment and treatment.
  • This study reviews the literature on ML techniques applied to suicidal behavior prediction.

Purpose of the Study:

  • To provide a comprehensive literature review of machine learning techniques used in suicidal behavior prediction.
  • To synthesize research on ML for suicide risk assessment.
  • To identify challenges and future research directions in the field.

Main Methods:

  • A systematic search of four databases (Web of Science, PubMed, Dimensions, Scopus) was conducted for papers published between January 2016 and September 2021.
  • Keywords included 'data mining,' 'machine learning,' and terms related to suicidal behavior (e.g., 'suicide,' 'suicidal ideation,' 'self-harm').
  • Included studies were synthesized based on country, sample characteristics, ML techniques used, features employed, and performance metrics.

Main Results:

  • Thirty-five empirical articles met the inclusion criteria for the review.
  • The review provides an overview of ML techniques, feature categories, and methodological challenges.
  • Ensemble prediction models demonstrated superior accuracy and utility compared to single prediction models.

Conclusions:

  • Machine learning holds significant potential for enhancing the estimation of future suicidal behavior and monitoring risk over time.
  • Further research is essential to overcome existing challenges and capitalize on opportunities for advancing suicide prediction.
  • Continued investigation can lead to significant improvements in identifying individuals at risk of suicide.