Machine learning in predicting recurrence of self-inflicted neck trauma in young adults

Machine learning in predicting recurrence of self-inflicted neck trauma in young adults. Download PDF Abstract Self-inflicted injuries, particularly among young adults, are associated with an increased risk of subsequent suicide attempts and suicide. Injuries to the anterior neck present unique challenges in management due to the presence of vital anatomical structures and possibly complicated consequences.

Therefore, this study employed machine learning (ML) techniques to predict injury recurrence in patients with self-inflected anterior neck injuries. The cohort comprised 167 individuals aged 18 to 30 years with self-inflicted injury to the anterior neck at the Helsinki University Hospital in 2005–2023. Patient records were systematically analyzed for patient demographics, clinical characteristics, psychiatric comorbidity, and clinical management for building a ML model.

Class imbalance and feature selection were handled across three different models; logistic regression, easy ensemble, and extreme gradient boosting-based models. Predictions were enhanced using SHapley Additive exPlanations (SHAP) for both global and local interpretability. Our results showed that the most important predictive variables for a recurrence were history of other trauma, patient age at presentation, and mode of arrival (walk-in versus brought in).

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