Abstract Purpose Traumatic brain injury (TBI) is one of the most common cause of mortality and disability globally. Intensive care unit (ICU) management poses significant challenges for medical practitioners, primarily because of the complex interplay between biomarkers and hidden interactions. This study aimed to uncover subtle interconnections between biomarkers and identify the key factors contributing to TBI characteristics and ICU severity scores. Methods A total of 29 patients with TBI who were admitted to the ICU were selected and analysed using monitoring electrocardiography (ECG), vital signs, Glasgow Coma Scale (GCS) and electronic medical records. This study utilized a methodology that integrates correlation-based network analysis and graph neural network (GNN) techniques to uncover hidden relationships between various biomarkers and identify the most critical monitoring biomarkers for patients with TBI within the first 12 hours of ICU stay. Results The analysis revealed significant associations within the dataset. Specifically, MeanRR exhibited notable connections with alterations in systolic blood pressure and heart rate variations. Moreover, the final GCS showed a strong correlation, including long-term correlation with heart rate variability (HRV) feature alpha2, variability in atrial blood pressure means and diastolic blood pressure, gender, and age. Variability of diastolic blood pressure, GCS ICU scoring values, and pNN50 (an HRV measure) demonstrated strong association with other biomarkers during the first 12 hours following ICU admission. Conclusion HRV as an electronic biomarker and the variability in physiological variables during first 12 hours in the ICU are equally important factors for TBI severity assessment and can offer valuable insights into the patient's health prognosis.

Electrocardiogram (ECG) signal analysis is widely used to diagnose various cardiac and non-cardiac diseases. Detecting abnormalities on ECG is critical for preventing the onset of life-threatening cardiac arrhythmias. This paper proposed a method based on deep convolutional neural network (DCNN) to detect abnormal heartbeats such as ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB). The proposed model was trained and validated on two large-sample PhysioNet’s MIT-BIH datasets. A separate test result showed overall accuracy of 96% on distinguishing three types of heartbeats VEB, SVEB, and other heartbeats which are not ectopic beat (NOTEB).

Cardiac arrest is associated with a high mortality, and current illness severity scores are limited with regards to predictive accuracy. Our objective was to use logistic regression and machine learning (ML) techniques develop and compare models for in-hospital mortality after cardiac arrest

BACKGROUND:Resuscitated cardiac arrest is associated with high mortality; however, the ability to estimate risk of adverse outcomes using existing illness severity scores is limited. Using in-hospital data available within the first 24 hours of admission, we aimed to develop more accurate models of risk prediction using both logistic regression (LR) and machine learning (ML) techniques, with a combination of demographic, physiologic, and biochemical information.METHODS AND FINDINGS:Patient-level data were extracted from the Australian and New Zealand Intensive Care Society (ANZICS) Adult Patient Database for patients who had experienced a cardiac arrest within 24 hours prior to admission to an intensive care unit (ICU) during the period January 2006 to December 2016. The primary outcome was in-hospital mortality. The models were trained and tested on a dataset (split 90:10) including age, lowest and highest physiologic variables during the first 24 hours, and key past medical history. LR and 5 ML approaches (gradient boosting machine [GBM], support vector classifier [SVC], random forest [RF], artificial neural network [ANN], and an ensemble) were compared to the APACHE III and Australian and New Zealand Risk of Death (ANZROD) predictions. In all, 39,566 patients from 186 ICUs were analysed. Mean (±SD) age was 61 ± 17 years; 65% were male. Overall in-hospital mortality was 45.5%. Models were evaluated in the test set. The APACHE III and ANZROD scores demonstrated good discrimination (area under the receiver operating characteristic curve [AUROC] = 0.80 [95% CI 0.79-0.82] and 0.81 [95% CI 0.8-0.82], respectively) and modest calibration (Brier score 0.19 for both), which was slightly improved by LR (AUROC = 0.82 [95% CI 0.81-0.83], DeLong test, p < 0.001). Discrimination was significantly improved using ML models (ensemble and GBM AUROCs = 0.87 [95% CI 0.86-0.88], DeLong test, p < 0.001), with an improvement in performance (Brier score reduction of 22%). Explainability models were created to assist in identifying the physiologic features that most contributed to an individual patient's survival. Key limitations include the absence of pre-hospital data and absence of external validation.CONCLUSIONS:ML approaches significantly enhance predictive discrimination for mortality following cardiac arrest compared to existing illness severity scores and LR, without the use of pre-hospital data. The discriminative ability of these ML models requires validation in external cohorts to establish generalisability.