Artificial intelligence for improved decision-making in diabetic emergency survival: A cross-sectional study

Aims: This study aims to develop and validate an artificial intelligence -driven survival prediction model using the Random Forest algorithm to support clinical decision-making in diabetic emergency cases. The model is designed to assist emergency nurses in triage prioritization and resource allocation to improve patient outcomes. Methods: A retrospective cross-sectional study was conducted using medical records of 1,047 diabetic emergency patients treated at regional hospital in Indonesia, from 2019 to 2024. Key clinical variables, including age, gender, blood glucose levels, Glasgow Coma Scale, triage classification, and insulin use, were analyzed. Logistic regression identified significant survival predictors, and random forest model was developed for survival prediction. Model performance was evaluated using accuracy, sensitivity, specificity, and the area under the receiver operating characteristic AUC (Area Under Curve). Results: The random forest model identified GCS and triage classification as the most significant predictors of survival. Patients with higher GCS scores and immediate triage classification (P1) had a greater likelihood of survival. The model demonstrated high predictive performance, achieving an accuracy of 94.9 %, sensitivity of 95.6 %, specificity of 93.7 %, and an AUC of 0.96. Conclusion: The AI-based random forest model demonstrated excellent predictive accuracy, supporting its integration into emergency nursing workflows. Implementing AI-driven decision-support systems in emergency departments may enhance triage accuracy, to improve survival outcomes in diabetic emergencies, future studies should focus on external validation and the integration of additional clinical parameters to further refine model performance.