Predictive modeling of suicidal ideation in patients with epilepsy

Abstract

Objective: The prevalence of suicide in the United States has seen an increasing trend and is responsible for 1.6% of all mortality nationwide. While suicide has the potential to broadly impact the entire population, it has a substantially increased prevalence in persons with epilepsy (PWE) despite many of these individuals consistently seeing a health care provider. The goal of this work is to predict the development of suicidal ideation in PWE using machine learning methodology such that providers can be better prepared to address suicidality at visits where it is likely to be prominent.

Methods: The current study leverages data collected at an epilepsy clinic during patient visits to predict whether an individual will exhibit suicidal ideation (SI) at their next visit. The data used for prediction consisted of patient responses to questions about the severity of their epilepsy, issues with memory/concentration, somatic problems, markers for mental health, and demographic information. A machine learning approach was then applied to predict whether an individual would display suicidal ideation at their following visit using only data collected at the prior visit.

Results: The modeling approach allowed for the successful prediction of an individual’s passive and active SI severity at the following visit (r=0.42, r=0.39) as well as the presence of SI regardless of severity (AUC=0.82, AUC=0.8). This shows that the model was successfully able to synthesize the unique combination of individual’s responses to important questions during a clinical visit and utilize that information indicate whether or not that individual will exhibit SI at their next visit.

Significance: The results of this modeling approach allow the healthcare team to be prepared, in advance to a clinical visit, for the potential endorsement of SI. By allowing the necessary support to be prepared ahead of time, it can be better integrated at the point-of-care, where patients are most likely to uptake potential referrals or treatment.