Development of a machine learning-based clinical decision support system to predict clinical deterioration in patients visiting the emergency department.

ABSTRACT: This study aimed to develop a machine learning-based clinical decision support system for emergency departments based on the decision-making framework of physicians. We extracted 27 fixed and 93 observation features using data on vital signs, mental status, laboratory results, and electrocardiograms during emergency department stay. Outcomes included intubation, admission to the intensive care unit, inotrope or vasopressor administration, and in-hospital cardiac arrest. eXtreme gradient boosting algorithm was used to learn and predict each outcome. Specificity, sensitivity, precision, F1 score, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve were assessed. We analyzed 303,345 patients with 4,787,121 input data, resampled into 24,148

SUBMITTER: Choi A 

PROVIDER: S-EPMC10220080 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature