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Predicting Suicide Attempt Trends in Youth: A Machine Learning Analysis Using Google Trends and Historical Data.

Zofia Kachlik1, Michał Walaszek1, Wojciech Nazar1

  • 1Department of Psychiatry, Faculty of Medicine, Medical University of Gdansk, 80-214 Gdańsk, Poland.

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Summary

Machine learning models using Google Trends data can help predict youth suicide attempts. This approach shows promise for real-time risk identification in paediatric populations.

Keywords:
forecastinggoogleinternetmachine learningonlinesuicide

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Area of Science:

  • Computational psychiatry
  • Digital epidemiology
  • Machine learning in public health

Background:

  • Suicide is a major cause of death in young people, with limited predictive tools available.
  • Predicting suicide attempts (SA) in individuals under 18 remains a significant challenge.
  • This study explores the use of Google Trends data for SA prediction in paediatric populations.

Purpose of the Study:

  • To develop and evaluate machine learning (ML) models for predicting SA in youth.
  • To identify reliable predictors of SA using online search data.
  • To assess the feasibility of using Google Trends for real-time suicide risk monitoring.

Main Methods:

  • Analysis of Relative Search Volumes (RSVs) from Google Trends for suicide risk-related terms.
  • Identification of terms strongly correlated with SA rates using Pearson Correlation Coefficients (PCC).
  • Development and evaluation of ML models including Random Forest Regression, Support Vector Regression (SVR), XGBoost, and Linear Regression, assessed by PCC, MAE, MSE, RMSE, and MAPE.

Main Results:

  • Terms like 'psychiatrist' and 'anxiety disorder' showed strong correlations with SA rates (PCC ≥ 0.90).
  • Random Forest Regression performed best (PCC = 0.953), identifying 'burnout,' 'anxiety disorder,' 'antidepressants,' and 'psychiatrist' as key predictors.
  • Other models showed varying performance: XGBoost (PCC = 0.446), SVR (PCC = 0.833), and Linear Regression (PCC = 0.947).

Conclusions:

  • ML models utilizing Google Trends data show potential for short-term prediction of youth SA.
  • Online search data can be a valuable tool for identifying real-time suicide risk in paediatric populations.
  • Further research is warranted to refine these predictive models and integrate them into public health strategies.