Additionally, the validation of ML-based CDSS remains challenging in clinical practice 17. However, previous studies have been limited to a narrow range of input data and were inappropriate for ED use since they dealt with broad and prolonged outcomes 5, 7. Therefore, it is crucial for CDSS to be able to assist the ED physicians who make time-critical decisions and interventions 4. Workflows are delayed in frequently crowded EDs 10, 11, 12, 13, making patients requiring time-critical interventions vulnerable to worse outcomes 14, 15, 16. Globally, increased ED visits have led to resource saturation and crowding 8, which affects both physicians and patients 9. Accordingly, attempts at developing ML-based CDSS that enable efficient prediction for clinical practice have been reported in the setting of emergency departments (EDs) 3, 5, 6, 7. The emergency care domain is particularly suitable for the challenge of adopting ML-based CDSS, because of the need for rapid clinical decision-making by physicians 4. Machine learning (ML) is being widely used in CDSS owing to its usefulness in diagnosis, prognosis, pattern recognition, and imaging classification with profound processing speed and the comprehensive nature of analytic methods 3. Machine learning-based clinical decision support systems customized according to clinical situations can help improve the quality of care.Ĭlinical decision support systems (CDSS) contribute to patient safety and improve clinical outcomes 1, 2. In this study, a human-centered approach to emulate the clinical decision-making process of emergency physicians has been adopted to enhance the use of the system. With inotropic use, intubation, and intensive care unit admission, the range of AUROC curve change with the leading 6 was the highest according to different amounts of previous information (lagging). The AUROC curve of in-hospital cardiac arrest had the smallest change, with increased lagging for all outcomes. The models displayed a discriminative ability to predict outcomes (AUROC > 0.9), and the model with lagging 6 and leading 0 displayed the highest value. Specificity, sensitivity, precision, F1 score, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve were assessed. eXtreme gradient boosting algorithm was used to learn and predict each outcome. Outcomes included intubation, admission to the intensive care unit, inotrope or vasopressor administration, and in-hospital cardiac arrest. We extracted 27 fixed and 93 observation features using data on vital signs, mental status, laboratory results, and electrocardiograms during emergency department stay. This study aimed to develop a machine learning-based clinical decision support system for emergency departments based on the decision-making framework of physicians.
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