Applying Machine Learning Approaches to Suicide Prediction Using Healthcare Data: Overview and Future Directions

Abstract

Objective: Early identification of individuals who are at risk for suicide is crucial in supporting suicide prevention. Machine learning is emerging as a promising approach to support this objective. Machine learning is broadly defined as a set of mathematical models and computational algorithms designed to automatically learn complex patterns between predictors and outcomes from example data, without being explicitly programmed to do so. The model's performance continuously improves over time by learning from newly available data.Method: This concept paper explores how machine learning approaches applied to healthcare data obtained from electronic health records, including billing and claims data, can advance our ability to accurately predict future suicidal behavior.Results: We provide a general overview of machine learning concepts, summarize exemplar studies, describe continued challenges, and propose innovative research directions.Conclusion: Machine learning has potential for improving estimation of suicide risk, yet important challenges and opportunities remain. Further research can focus on incorporating evolving methods for addressing data imbalances, understanding factors that affect generalizability across samples and healthcare systems, expanding the richness of the data, leveraging newer machine learning approaches, and developing automatic learning systems.

Prediction of adolescent suicide attempt by integrating clinical, neurocognitive and geocoded neighborhood environment data

“…Participants underwent deep phenotyping that included an in-person clinical interview (for participants older than 10), and interview with the primary caregiver (for participants aged [8][9][10][11][12][13][14][15][16][17]. Thus, ages 8-10 received only caregiver interviews and ages 11-17 received both caregiver and self-report.…”

“…Research is now in the phase of determining the optimal way of leveraging such prediction algorithms into translational tools (14). A few promising studies suggest that ML predictive modeling can yield predictions of clinical relevance (15), mostly relying on the electronic health record [EHR] (16). Predicting SA in youth based on EHR data may be challenging, because the information contained in the average youth’s EHR is substantially less extensive than that contained in the typical adult’s EHR.…”

Prediction model for suicide based on back propagation neural network and multilayer perceptron

“…They have been widely used in engineering and scientific research fields. With the development of AI and computer technology, there is a few of research have tried to apply the machine learning method to suicide and suicide attempt ( Mezuk et al, 2018 ; Ryu et al, 2019 ; Chen et al, 2020 ; Gradus et al, 2020 ; Boudreaux et al, 2021 ). But there are few studies establishing the prediction model for suicide using the ANN.…”

A machine-learning model to predict suicide risk in Japan based on national survey data

“…For the past 50 years, extensive work has been conducted to improve the prediction of suicide, yet a recent published meta-analysis demonstrated that using known suicide risk factors leads to modest results (weighted area under the receiver operating characteristic curve [AUC], 0.58) ( 2 ). Several factors may have led to this prediction failure ( 3 ). Firstly, suicidal rate in the population is relatively low, making prospective studies not practical ( 1 ).…”

“…Second, prior studies were often limited to small samples, measured at a single time point, and examined few number of factors. Finally, the traditional method for statistical analysis of the suicide data mainly focus on inference, which resulted in simple prediction models; lastly, they are not designed to incorporate new clinical data to continuously update the existing models ( 3 ).…”