Dynamic Prediction of Emergency Department Admissions Using Explainable Artificial Intelligence: A Large-Scale Temporal Validation Study
- 1İbni Sina Family Health Center, Erzurum 25700, Türkiye
- 2Biostatistics and Medical İnformatics Department, Faculty of Medicine, İnönü University, Malatya 44280, Türkiye
Abstract
Background: The clinical adoption of artificial intelligence in emergency departments (ED) is hindered by the “black-box” nature of complex models and retrospective data leakage from downstream laboratory results. Aims: We aimed to develop a transparent, dynamic machine learning model to predict hospital admissions by establishing a pre-laboratory “information ceiling” using structured electronic health record (EHR) data. Study Design: Retrospective cohort study. Methods: Utilizing 387,407 patient encounters in the MIMIC-IV-ED (v2.2) database, we developed a dynamic Explainable Boosting Machine (EBM) validated across chronologically distinct anchor_year_group blocks (2008–2016 training vs. 2017–2019 testing) with strict patient-level separation. Anchoring clock-zero (T = 0) at ED arrival (intime) with baseline triage parameters and pre-existing historical records, the model evaluated patients sequentially in 30 min landmarks up to 120 min (T = 120), incorporating timestamped physiological volatility and early medication administrations (pyxis) recorded strictly prior to the prediction landmark and disposition decision. Predictive performance was compared against a black-box HistGradientBoosting (HGB) model and an L2-regularized logistic regression baseline. Results: The dynamic EBM provided strong baseline discrimination at T = 0 with an ROC-AUC of 0.8048 [95% CI: 0.800–0.806] and a PR-AUC of 0.7395 [95% CI: 0.732–0.742]. Incorporating longitudinal physiological trajectories over a two-hour window yielded a modest, statistically significant improvement at T = 120 (ROC-AUC: 0.8168 [95% CI: 0.813–0.819], PR-AUC: 0.7595 [95% CI: 0.754–0.763], Sensitivity: 80.74%, Specificity: 65.09%, Positive Predictive Value: 61.16%; ΔROC-AUC = +0.0120, p < 0.0001). At T = 120, the opaque HGB benchmark achieved a slightly higher ROC-AUC of 0.8208 [95% CI: 0.817–0.824] (ΔROC-AUC = −0.0040, p = 0.0018), whereas EBM substantially outperformed the linear logistic regression baseline (ROC-AUC: 0.7420 [95% CI: 0.738–0.746]). Without relying on post hoc approximations, EBM generated directly inspectable shape functions capturing non-linear clinical risk relationships, including an observational risk inflection at five home medications and substantial risk penalties for transient hypoxic dips and extreme pulse pressures. Conclusions: By modeling early ED evaluation as a dynamic trajectory, the EBM achieves discriminative performance closely approaching black-box ensemble methods while maintaining full architectural transparency. Although predicting historical admission decisions requires prospective validation before clinical deployment, this calibrated glass-box framework offers an interpretable foundation for early risk stratification in the ED.
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BibTeX
@article{sakiroglu2026dynamic,
title = {{Dynamic Prediction of Emergency Department Admissions Using Explainable Artificial Intelligence: A Large-Scale Temporal Validation Study}},
author = {Furkan Şakiroğlu and Cemil Çolak},
journal = {Journal of Clinical Medicine},
year = {2026},
month = nov,
doi = {10.3390/jcm15218694},
url = {https://www.mdpi.com/2077-0383/15/21/8694},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Dynamic Prediction of Emergency Department Admissions Using Explainable Artificial Intelligence: A Large-Scale Temporal Validation Study
AU - Furkan Şakiroğlu
AU - Cemil Çolak
PY - 2026
DA - 2026/11/07
JO - Journal of Clinical Medicine
DO - 10.3390/jcm15218694
UR - https://www.mdpi.com/2077-0383/15/21/8694
AB - Background: The clinical adoption of artificial intelligence in emergency departments (ED) is hindered by the “black-box” nature of complex models and retrospective data leakage from downstream laboratory results. Aims: We aimed to develop a transparent, dynamic machine learning model to predict hospital admissions by establishing a pre-laboratory “information ceiling” using structured electronic health record (EHR) data. Study Design: Retrospective cohort study. Methods: Utilizing 387,407 patient encounters in the MIMIC-IV-ED (v2.2) database, we developed a dynamic Explainable Boosting Machine (EBM) validated across chronologically distinct anchor_year_group blocks (2008–2016 training vs. 2017–2019 testing) with strict patient-level separation. Anchoring clock-zero (T = 0) at ED arrival (intime) with baseline triage parameters and pre-existing historical records, the model evaluated patients sequentially in 30 min landmarks up to 120 min (T = 120), incorporating timestamped physiological volatility and early medication administrations (pyxis) recorded strictly prior to the prediction landmark and disposition decision. Predictive performance was compared against a black-box HistGradientBoosting (HGB) model and an L2-regularized logistic regression baseline. Results: The dynamic EBM provided strong baseline discrimination at T = 0 with an ROC-AUC of 0.8048 [95% CI: 0.800–0.806] and a PR-AUC of 0.7395 [95% CI: 0.732–0.742]. Incorporating longitudinal physiological trajectories over a two-hour window yielded a modest, statistically significant improvement at T = 120 (ROC-AUC: 0.8168 [95% CI: 0.813–0.819], PR-AUC: 0.7595 [95% CI: 0.754–0.763], Sensitivity: 80.74%, Specificity: 65.09%, Positive Predictive Value: 61.16%; ΔROC-AUC = +0.0120, p < 0.0001). At T = 120, the opaque HGB benchmark achieved a slightly higher ROC-AUC of 0.8208 [95% CI: 0.817–0.824] (ΔROC-AUC = −0.0040, p = 0.0018), whereas EBM substantially outperformed the linear logistic regression baseline (ROC-AUC: 0.7420 [95% CI: 0.738–0.746]). Without relying on post hoc approximations, EBM generated directly inspectable shape functions capturing non-linear clinical risk relationships, including an observational risk inflection at five home medications and substantial risk penalties for transient hypoxic dips and extreme pulse pressures. Conclusions: By modeling early ED evaluation as a dynamic trajectory, the EBM achieves discriminative performance closely approaching black-box ensemble methods while maintaining full architectural transparency. Although predicting historical admission decisions requires prospective validation before clinical deployment, this calibrated glass-box framework offers an interpretable foundation for early risk stratification in the ED.
SN - 2077-0383
ER -