Interpretable Machine Learning to Anticipate the Diagnostic Yield of EEG in the Emergency department. The EMINENCE study.

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: AUC 0.79-0.84 across different prediction tasks

• Primary Outcome: AUC for predicting abnormal emEEG, epileptiform activity, and diagnostic confirmation/refutation

• Nnt Or Sensitivity: Sensitivity and specificity not reported, only AUC values provided

• Confidence Interval: 95% CI: 0.76-0.86 depending on outcome

Clinical Application

High feasibility with web-based tool using routine admission data, though requires validation in Australian EDs Relevant to Australian emergency medicine practice. Would need validation against local ED populations and integration with existing neurological referral pathways. No specific PBS or TGA implications as this is a diagnostic prediction tool. Emergency department patients requiring EEG for altered consciousness or seizure evaluation

Abstract

INTRODUCTION: Emergent electroencephalography (emEEG) is increasingly employed in the emergency department (ED) for evaluating altered consciousness and seizure-related conditions, yet standardized criteria guiding its use remain limited. METHODS: We retrospectively analyzed 1,018 patients (mean age 66 ± 20 years; 48.4% female) undergoing emEEG at the ED of the Careggi Teaching Hospital (Florence, Italy) in 2023. Clinical, anamnestic, and neuroimaging data available at admission were used to train supervised machine-learning (ML) models. We evaluated tree-based ensembles (Random Forest and XGBoost) to predict abnormal and epileptiform emEEG, as well as confirmation or refutation of initial diagnosis. Ground-truth labels were derived from a multidisciplinary expert team including neurologists, neurophysiopathologists and intensivists. Model performance was assessed with 5 × 5 nested cross-validation, receiver operating characteristic (ROC) analysis, balanced accuracy, decision-curve analysis, and Shapley Additive Explanations (SHAP) interpretability. RESULTS: Abnormal emEEG occurred in 691 cases (67.9%), epileptiform activity in 192 patients (18.9%). emEEG ruled out the initial diagnostic suspicion in 514 cases (50.5%) and confirmed it in 188 cases (18.5%). Best performance was obtained with Random Forest for abnormal emEEG (AUC 0.79, 95% CI: 0.76-0.82) and diagnosis rule-out (0.84, 0.81-0.86), and with XGBoost for epileptiform emEEG (0.82, 0.78-0.85) and diagnosis confirmation (0.82, 0.79-0.85). Performance varied by initial diagnostic suspicion, but subgroup-stratified analyses showed overall consistent patterns. Key predictive features included altered consciousness, prior brain lesions, antiseizure therapy, and seizure-related presentations. Interpretability analyses revealed seizure-centric features drove confirmation, while systemic or nonspecific features favored refutation. CONCLUSIONS: Interpretable ML models using only admission data can predict emEEG outcomes and anticipate their diagnostic contribution, supporting triage and decision-making in emergency neurology without replacing clinical judgment. Models and explanations were easily usable on a freely-accessible website ( www.emergencyeeg.com ), where tools return probabilistic outputs for all four prediction tasks together with per-patient explanation plots, enabling transparent and reproducible use. CLINICAL TRIAL NUMBER: Not applicable.

References

1. 1.Scarpino, M., Marra, E., Liuzzi, P., Piccardi, B., Nazerian, P., Sgrilli, I., Mannini, A., Nencioni, A., & Grippo, A. (2026). Interpretable Machine Learning to Anticipate the Diagnostic Yield of EEG in the Emergency department. The EMINENCE study. Journal of Medical Systems. https://doi.org/10.1177/0272989X12458348