The Sydney Triage to Admission Risk Tool With Artificial Intelligence (START-AI) to Support Decision Making in Emergency Departments: Model Explainability and Feature Importance Analysis.

OBJECTIVE: Evaluate the importance of specific variables contributing to a recently reported Artificial Intelligence (AI) prediction model called Sydney Triage to Admission Risk Tool with Artificial Intelligence (START-AI) to predict inpatient admission from the Emergency Department (ED). METHODS: A model explainability analysis was undertaken using single-centre ED electronic medical record data over 2 years. The START-AI model, which comprises ensemble machine learning and a transformer-based algorithm to enhance the original START tool, was re-run with each feature added sequentially to the model until the full model was complete. Feature importance was calculated using feature permutation or change in area under receiver operator curve (AUROC) and SHapley Additive exPlanations (SHAP) analyses. RESULTS: The original START tool alone had an AUROC of 0.78 (95% CI 0.77, 0.78) for prediction of inpatient admission, reaching an AUROC of 0.90 (95% CI 0.89, 0.90) after all features of the START-AI tool were added sequentially. Features associated with a stepwise increase in cumulative AUROC were triage comments, ED case history notes, any blood test result (in particular, lactate and C-reactive protein), and any CT order. Vital signs did not appear to be associated with stepwise increases in AUROC. Feature importance was highest for the presence of any blood test result, START score, and C-reactive protein with respect to overall model importance. CONCLUSION: A model explainability analysis provided a clearer understanding of the sequential and relative importance of specific features within the START-AI model, informing how the tool can be further developed and deployed in clinical settings.