A Hybrid Bidirectional Deep Learning Model Using HRV for Prediction of ICU Mortality Risk in TBI Patients.

ABSTRACT: Accurately predicting early mortality risk for traumatic brain injury (TBI) patients admitted to the intensive care unit (ICU) is crucial for optimizing patient care, allocating resources effectively, and reducing mortality rates. This study introduces an approach to predict mortality risk for TBI patients by analysing heart rate variability from the first 24 h of electrocardiogram (ECG) signals. A deep learning hybrid model was developed by integrating a weight predictor with a bidirectional long short-term memory (BiLSTM) unit. This hybrid architecture enhances predictive performance by weighting features and capturing patterns in HRV data. This study utilised TBI patient data from the Gold Coast University Hospital and Cerebral Haemodynamic Autoregulatory Information System (CHARIS) for

SUBMITTER: Kuruwita A H 

PROVIDER: S-EPMC12602819 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature