Improving risk prediction for target subpopulations: Predicting suicidal behaviors among multiple sclerosis patients

Yuval Barak-Corren, Victor M Castro, Solomon Javitt, Matthew K Nock, Jordan W Smoller and Ben Y Reis

PLOS ONE, 2023, vol. 18, issue 2, 1-10

Abstract: Several recent studies have applied machine learning techniques to develop risk algorithms that predict subsequent suicidal behavior based on electronic health record data. In this study we used a retrospective cohort study design to test whether developing more tailored predictive models—within specific subpopulations of patients—would improve predictive accuracy. A retrospective cohort of 15,117 patients diagnosed with multiple sclerosis (MS), a diagnosis associated with increased risk of suicidal behavior, was used. The cohort was randomly divided into equal sized training and validation sets. Overall, suicidal behavior was identified among 191 (1.3%) of the patients with MS. A Naïve Bayes Classifier model was trained on the training set to predict future suicidal behavior. With 90% specificity, the model detected 37% of subjects who later demonstrated suicidal behavior, on average 4.6 years before the first suicide attempt. The performance of a model trained only on MS patients was better at predicting suicide in MS patients than that a model trained on a general patient sample of a similar size (AUC of 0.77 vs. 0.66). Unique risk factors for suicidal behavior among patients with MS included pain-related codes, gastroenteritis and colitis, and history of smoking. Future studies are needed to further test the value of developing population-specific risk models.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0277483

DOI: 10.1371/journal.pone.0277483

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