Machine learning for suicide risk prediction in children and adolescents with electronic health records

Abstract

Accurate prediction of suicide risk among children and adolescents within an actionable time frame is an important but challenging task. Very few studies have comprehensively considered the clinical risk factors available to produce quantifiable risk scores for estimation of short- and long-term suicide risk for pediatric population. In this paper, we built machine learning models for predicting suicidal behavior among children and adolescents based on their longitudinal clinical records, and determining short- and long-term risk factors. This retrospective study used deidentified structured electronic health records (EHR) from the Connecticut Children’s Medical Center covering the period from 1 October 2011 to 30 September 2016. Clinical records of 41,721 young patients (10–18 years old) were included for analysis. Candidate predictors included demographics, diagnosis, laboratory tests, and medications. Different prediction windows ranging from 0 to 365 days were adopted. For each prediction window, candidate predictors were first screened by univariate statistical tests, and then a predictive model was built via a sequential forward feature selection procedure. We grouped the selected predictors and estimated their contributions to risk prediction at different prediction window lengths. The developed predictive models predicted suicidal behavior across all prediction windows with AUCs varying from 0.81 to 0.86. For all prediction windows, the models detected 53–62% of suicide-positive subjects with 90% specificity. The models performed better with shorter prediction windows and predictor importance varied across prediction windows, illustrating short- and long-term risks. Our findings demonstrated that routinely collected EHRs can be used to create accurate predictive models for suicide risk among children and adolescents.

A review and meta-analysis of machine intelligence approaches for mental health issues and depression detection

“…In 2020, Su et al [62] developed a machine model for the prediction of suicidal behavior in children and adolescents. They used the data of 129,485 patients from Connecticut Children's Medical Center (CCMC) HER database from October 1, 2011 to September 30, 2016.…”

Classification of Adolescent Psychiatric Patients at High Risk of Suicide Using the Personality Assessment Inventory by Machine Learning

“…14 Recently, some studies have applied machine learning in the prediction of suicide, 15 and there was an attempt to classify or predict adolescents with a suicide risk using machine learning. [15][16][17] However, none of the studies attempted to predict adolescents with high suicide risk using machine learning in consideration of their personality traits. Thus, this study aims to classify groups at risk of suicide by including personality traits using the PAI.…”

“…In addition, most studies simply examined the association of suicide risk with symptoms such as depression and anxiety or identified relevant risk factors [ 14 ]. Recently, some studies have applied machine learning in the prediction of suicide [ 15 ], and there was an attempt to classify or predict adolescents with a suicide risk using machine learning [ 15 - 17 ]. However, none of the studies attempted to predict adolescents with high suicide risk using machine learning in consideration of their personality traits.…”

Incidence Trends and Risk Prediction Nomogram for Suicidal Attempts in Patients With Major Depressive Disorder

“…In China the percentage of MDD with SA is reported to be between 14.3 and 25% (13). Previous studies explored the prediction of suicide by machine learning (14)(15)(16), and the risk factors of suicidal thought in adults based on decision tree analysis (17). However, these studies do not provide sufficient information on clinical implications to be implemented.…”