# Predicting Adolescent Suicide Attempts with Neural Networks

**Authors:** Harish S. Bhat, Sidra J. Goldman-Mellor  
**arXiv:** [1711.10057](https://arxiv.org/abs/1711.10057)  
**Date:** 2017-11-28  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/1711.10057v2/

## Key points

- Suicide is a major public health issue in the United States.
- Machine learning methods have not been widely used to predict an individual's risk of attempting or committing suicide.
- Harish S. Bhat and Sidra J. Goldman-Mellor developed neural network models to predict suicide attempts among California-resident adolescents using anonymized electronic health records for 522,056 unique patients.
- The data from 2006-2009 was used to frame the problem as a binary classification task where the presence (1) or absence (0) of a suicide attempt in 2010 was predicted.
- Neural networks with depths ranging from two to eight hidden layers were built to tackle issues such as imbalanced classes and variable patient history lengths.
- The depth-4 model achieved a sensitivity of 0.703, specificity of 0.980, and AUC of 0.958 on test set observations where there were at least five ED/hospital visits' worth of data on a patient.
- This research shows the potential for machine learning methods to be applied in predicting suicide attempts and emphasizes the importance of addressing challenges such as imbalanced classes and variable patient history lengths when developing predictive models for mental health outcomes.

## Summary

Suicide is a major public health issue in the United States and machine learning methods have not been widely used to predict an individual's risk of attempting or committing suicide. To address this problem, Harish S. Bhat and Sidra J. Goldman-Mellor developed neural network models to predict suicide attempts among California-resident adolescents using anonymized electronic health records for 522,056 unique patients. The data from 2006-2009 was used to frame the problem as a binary classification task where the presence (1) or absence (0) of a suicide attempt in 2010 was predicted. To tackle issues such as imbalanced classes and variable patient history lengths, neural networks with depths ranging from two to eight hidden layers were built. The depth-4 model achieved a sensitivity of 0.703, specificity of 0.980, and AUC of 0.958 on test set observations where there were at least five ED/hospital visits' worth of data on a patient. This research shows the potential for machine learning methods to be applied in predicting suicide attempts and emphasizes the importance of addressing challenges such as imbalanced classes and variable patient history lengths when developing predictive models for mental health outcomes.