Machine Learning Model Reveals Determinators for Admission to Acute Mental Health Wards From Emergency Department Presentations

Abstract

This research addresses the critical issue of identifying factors contributing to admissions to acute mental health (MH) wards for individuals presenting to the emergency department (ED) with MH concerns as their primary issue, notably suicidality. This study aims to leverage machine learning (ML) models to assess the likelihood of admission to acute MH wards for this vulnerable population. Data collection for this study used existing ED data from 1 January 2016 to 31 December 2021. Data selection was based on specific criteria related to the presenting problem. Analysis was conducted using Python and the Interpretable Machine Learning (InterpretML) machine learning library. InterpretML calculates overall importance based on the mean absolute score, which was used to measure the impact of each feature on admission. A person's 'Age' and 'Triage category' are ranked significantly higher than 'Facility identifier', 'Presenting problem' and 'Active Client'. The contribution of other presentation features on admission shows a minimal effect. Aligning the models closely with service delivery will help services understand their service users and provide insight into financial and clinical variations. Suicidal ideation negatively correlates to admission yet represents the largest number of presentations. The nurse's role at triage is a critical factor in assessing the needs of the presenting individual. The gap that emerges in this context is significant; MH triage requires a complex understanding of MH and presents a significant challenge in the ED. Further research is required to explore the role that ML can provide in assisting clinicians in assessment.