Early identification of high-risk patients admitted to emergency departments using vital signs and machine learning.

Background: Rapid and accurate identification of high-risk patients in the emergency departments (EDs) is crucial for optimizing resource allocation and improving patient outcomes. This study aimed to develop an early prediction model for identifying high-risk patients in EDs using initial vital sign measurements.

Methods: This retrospective cohort study analyzed initial vital signs from the Chinese Emergency Triage, Assessment, and Treatment (CETAT) database, which was collected between January 1^st, 2020, and June 25^th, 2023. The primary outcome was the identification of high-risk patients needing immediate treatment. Various machine learning methods, including a deep-learning-based multilayer perceptron (MLP) classifier were evaluated. Model performance was assessed using the area under the receiver operating characteristic curve (AUC-ROC). AUC- ROC values were reported for three scenarios: a default case, a scenario requiring sensitivity greater than 0.8 (Scenario I), and a scenario requiring specificity greater than 0.8 (Scenario II). SHAP values were calculated to determine the importance of each predictor within the MLP model.

Results: A total of 38,797 patients were analyzed, of whom 18.2% were identified as high-risk. Comparative analysis of the predictive models for high-risk patients showed AUC-ROC values ranging from 0.717 to 0.738, with the MLP model outperforming logistic regression (LR), Gaussian Naive Bayes (GNB), and the National Early Warning Score (NEWS). SHAP value analysis identified coma state, peripheral capillary oxygen saturation (SpO₂), and systolic blood pressure as the top three predictive factors in the MLP model, with coma state exerting the most contribution.

Conclusion: Compared with other methods, the MLP model with initial vital signs demonstrated optimal prediction accuracy, highlighting its potential to enhance clinical decision-making in triage in the EDs.