Int J Med Inform. 2026 May 14;216:106483. doi: 10.1016/j.ijmedinf.2026.106483. Online ahead of print.
ABSTRACT
BACKGROUND: The sustainability of service quality in healthcare systems is directly related to accurate resource planning, especially in emergency departments with high unpredictability. This study aims to analyze the impact of meteorological factors on emergency department visits and propose a highly accurate and explainable artificial intelligence-based decision support model for hospital management. Within the scope of the research, a large dataset of approximately 1.5 million records from two different public hospitals in the Eastern Black Sea region of Turkey was used.
METHODS: As a method, comprehensive feature engineering was performed on the raw data; calendar variables, meteorological lags, and historical application trends were derived. The meaningfulness of the input variables was initially verified using Correlation and Granger Causality analyses, and the final variable selection was performed using the SHAP (SHapley Additive exPlanations) method, which is an explainable artificial intelligence (XAI) approach. The SHAP-based feature selection step was performed independently as a pre-processing filter, and the resulting feature set was then locked and applied uniformly to all 22 models, ensuring a fair comparison. In the study, a total of 22 models from three different groups, including Machine Learning, Deep Learning, and Time Series methods, were tested comparatively.
RESULTS: A scenario-based evaluation strategy was followed to measure the adaptation of models to dynamic data structures. According to the findings, the 7-day "Walk-Forward" (WF-7d) update scenario, which simulates real-life conditions, emerged as the most optimal strategy, reducing the average error by 9.62% compared to static models. The Prophet model, which demonstrated the best performance, achieved the highest success with values of 34.55 ± 5.66 MAE (5.54% ± 1.41% MAPE) in the Ordu State Hospital data and 45.84 ± 7.51 MAE (5.47% ± 1.39% MAPE) in the Education and Research Hospital data. Additionally, it was found that the SVM and CatBoost models, which have low error rates, maintained generalizability in both institutions.
CONCLUSION: The proposed system has the potential to increase operational efficiency by providing healthcare administrators with a proactive decision support mechanism for critical processes ranging from staff scheduling to bed capacity management.
PMID:42143518 | DOI:10.1016/j.ijmedinf.2026.106483