Prediction of mortality in intensive care unit with short-term heart rate variability: Machine learning-based analysis of the MIMIC-III database.
Journal:
Computers in biology and medicine
PMID:
39778237
Abstract
BACKGROUND: Prognosis prediction in the intensive care unit (ICU) traditionally relied on physiological scoring systems based on clinical indicators at admission. Electrocardiogram (ECG) provides easily accessible information, with heart rate variability (HRV) derived from ECG showing prognostic value. However, few studies have conducted a comprehensive analysis of HRV-based prognostic model against established standards, which limits the application of HRV's prognostic value in clinical settings. This study aims to evaluate the utility of HRV in predicting mortality in the ICU. Additionally, we analyzed the applicability and interpretability of the HRV-integrated clinical model and identified the HRV factors that are most significant for patient prognosis.