Prediction of Out-of-Hospital Cardiac Arrest Survival Outcomes Using a Hybrid Agnostic Explanation TabNet Model

Abstract

Survival after out-of-hospital cardiac arrest (OHCA) is contingent on time-sensitive interventions taken by onlookers, emergency call operators, first responders, emergency medical services (EMS) personnel, and hospital healthcare staff. By building integrated cardiac resuscitation systems of care, measurement systems, and techniques for assuring the correct execution of evidence-based treatments by bystanders, EMS professionals, and hospital employees, survival results can be improved. To aid in OHCA prognosis and treatment, we develop a hybrid agnostic explanation TabNet (HAE-TabNet) model to predict OHCA patient survival. According to the results, the HAE-TabNet model has an “Area under the receiver operating characteristic curve value” (ROC AUC) score of 0.9934 (95% confidence interval 0.9933–0.9935), which outperformed other machine learning models in the previous study, such as XGBoost, k-nearest neighbors, random forest, decision trees, and logistic regression. In order to achieve model prediction explainability for a non-expert in the artificial intelligence field, we combined the HAE-TabNet model with a LIME-based explainable model. This HAE-TabNet model may assist medical professionals in the prognosis and treatment of OHCA patients effectively.

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