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Med."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Predicting in-hospital cardiac arrest in patients admitted to an intensive care unit (ICU) allows prompt interventions to improve patient outcomes. We developed and validated a machine learning-based real-time model for in-hospital cardiac arrest predictions using electrocardiogram (ECG)-based heart rate variability (HRV) measures. The HRV measures, including time\/frequency domains and nonlinear measures, were calculated from 5\u2009min epochs of ECG signals from ICU patients. A light gradient boosting machine (LGBM) algorithm was used to develop the proposed model for predicting in-hospital cardiac arrest within 0.5\u201324\u2009h. The LGBM model using 33 HRV measures achieved an area under the receiver operating characteristic curve of 0.881 (95% CI: 0.875\u20130.887) and an area under the precision-recall curve of 0.104 (95% CI: 0.093\u20130.116). The most important feature was the baseline width of the triangular interpolation of the RR interval histogram. 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