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.
@article{arxiv.1711.10057,
title = {Predicting Adolescent Suicide Attempts with Neural Networks},
author = {Harish S. Bhat and Sidra J. Goldman-Mellor},
journal= {arXiv preprint arXiv:1711.10057},
year = {2017}
}
Comments
Accepted poster at NIPS 2017 Workshop on Machine Learning for Health (https://ml4health.github.io/2017/)