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Applying machine learning on health record data from general practitioners to predict suicidality.

Kasper van Mens1,2, Elke Elzinga3, Mark Nielen4

  • 1Altrecht Mental Healthcare, Utrecht, the Netherlands.

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Summary

Machine learning (ML) models can help general practitioners (GPs) identify patients at risk of suicidal behavior using routinely collected primary care data. This approach offers a promising tool for early detection and intervention in clinical practice.

Keywords:
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Area of Science:

  • Medical Informatics
  • Public Health
  • Machine Learning in Healthcare

Background:

  • Suicidal behavior detection in general practice is challenging.
  • Machine learning (ML) algorithms can potentially aid General Practitioners (GPs) in identifying patients at risk.
  • This study explores ML application on routinely collected primary care data.

Purpose of the Study:

  • To apply machine learning techniques to support GPs in recognizing suicidal behavior.
  • To utilize routinely collected general practice data for predicting suicidal behavior.
  • To evaluate the effectiveness of ML in identifying at-risk primary care patients.

Main Methods:

  • A case-control study utilized a national representative primary care database (Nivel Primary Care Database) with over 1.5 million patients.
  • Cases included patients with a suicide attempt in 2017 (N=574); controls were selected from patients with psychological vulnerability but no suicide attempt (N=207,308).
  • The RandomForest algorithm was trained on a subsample and evaluated on unseen data.

Main Results:

  • 65% of cases visited their GP within 30 days prior to the suicide attempt.
  • RandomForest achieved a positive predictive value (PPV) of 0.05, sensitivity of 0.39, and AUC of 0.85.
  • The model demonstrated high specificity (0.98) in identifying controls, with potential to flag high-risk patients for further review.

Conclusions:

  • Machine learning techniques show potential as a complementary tool for identifying and stratifying patients at risk of suicidal behavior.
  • The results are encouraging for the direct use of automated screening in clinical practice.
  • Incorporating additional data domains like employment and education may further enhance predictive accuracy.