# Predicting Adolescent Suicide Attempts with Neural Networks

arXiv: https://arxiv.org/abs/1711.10057 · HTML: https://mlsanity.com/paper/1711.10057/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2017.

## Built on by (most cited first, 2 of 2)

- [ScAN: Suicide Attempt and Ideation Events Dataset](https://mlsanity.com/paper/2205.07872/) (2022)
- [Handling Extreme Class Imbalance: Using GANs in Data Augmentation for Suicide Prediction](https://mlsanity.com/paper/2510.17661/) (2025)

## Abstract (arXiv)

Though suicide is a major public health problem in the US, machine learning methods are not commonly used to predict an individual's risk of attempting/committing suicide. In the present work, starting with an anonymized collection of electronic health records for 522,056 unique, California-resident adolescents, we develop neural network models to predict suicide attempts. We frame the problem as a binary classification problem in which we use a patient's data from 2006-2009 to predict either the presence (1) or absence (0) of a suicide attempt in 2010. After addressing issues such as severely imbalanced classes and the variable length of a patient's history, we build neural networks with depths varying from two to eight hidden layers. For test set observations where we have at least five ED/hospital visits' worth of data on a patient, our depth-4 model achieves a sensitivity of 0.703, specificity of 0.980, and AUC of 0.958.