Critical Care

OMNI: Optimized Multi-view Network Integration with Heterogeneous Graph Attention for Biomedical Interaction Prediction

Accurate prediction of biomedical relationships, such as chemical–gene interactions, is fundamental to understanding disease mechanisms and advancing drug discovery. With the rapid growth of heterogeneous biological data, modeling large-scale, multi-entity networks has become increasingly challenging. Traditional approaches, including homogeneous GNNs (e.g., GCN, GAT) and meta-path-based random wa...

Protocol for the development and validation of machine-learning models for predicting the risk of hypertriglyceridemia in critically ill patients receiving propofol sedation using retrospective data

Propofol is a widely used sedative-hypnotic agent for critically-ill patients requiring invasive mechanical ventilation (IMV). Despite its clinical benefits, propofol is associated with increased risks of hypertriglyceridemia. Early identification of patients at risk for propofol-associated hypertriglyceridemia is crucial for optimizing sedation strategies and preventing adverse outcomes. Machine ...

Care Phenotypes In Critical Care

The Social Determinants of Health (SDoH) have long been recognised as significant drivers of health inequalities. Within healthcare settings, large EH...

Modeling trajectories of routine blood tests as dynamic biomarkers for outcome in spinal cord injury

Early outcome prediction after acute traumatic spinal cord injury (SCI) is challenging due to pathological complexities and population heterogeneity. ...

Deep Learning–Based Early Detection of Major Adverse Cerebral Injuries in Cardiothoracic and Vascular Surgery

Despite advances in central nervous system (CNS)-protective anesthetic and surgical strategies, perioperative stroke remains a significant concern in ...

ORAKLE: Optimal Risk prediction for mAke30 in patients with acute Kidney injury using deep Learning

Major Adverse Kidney Events within 30 days (MAKE30) is an important patient-centered outcome for assessing the impact of acute kidney injury (AKI). Th...

Summarizing Clinical Notes using LLMs for ICU Bounceback and Length-of-Stay Prediction

Recent advances in the Large Language Models (LLMs) provide a promising avenue for retrieving relevant information from clinical notes for accurate ri...

A pragmatic randomized controlled trial of artificial intelligence (AI)-based predictive analytics monitoring for early detection of clinical deterioration

This pragmatic randomized controlled trial aimed to assess the effect of a passive display of artificial intelligence (AI)-based predictive analytics ...

ICU Readmission Prediction for Intracerebral Hemorrhage Patients using MIMIC III and MIMIC IV Databases

Intracerebral hemorrhage (ICH) is a critical form of stroke resulting from bleeding within the brain, with a mortality rate of 40-50% within a few day...

Towards AI-based Precision Rehabilitation via Contextual Model-based Reinforcement Learning

Stroke is a condition marked by considerable variability in lesions, recovery trajectories, and responses to therapy. Consequently, precision medicine...

Machine Learning-Based Prediction of ICU Readmissions in Intracerebral Hemorrhage Patients: Insights from the MIMIC Databases

Intracerebral hemorrhage (ICH) is a life-risking condition characterized by bleeding within the brain parenchyma. ICU readmission in ICH patients is a...

A Multi-pathogen Hospitalization Forecasting Model for the United States: An Optimized Geo-Hierarchical Ensemble Framework

Accurate forecasting of infectious diseases is crucial for timely public health response. Ensemble frameworks have shown promising outcomes in short-t...

Computational Phenomapping of Randomized Clinical Trials to Enable Assessment of their Real-world Representativeness and Personalized Inference

Randomized clinical trials (RCTs) define evidence-based medicine, but quantifying their generalizability to real-world patients remains challenging. W...

Multi-resolution vision transformer model for skin cancer subtype classification using histopathology slides

Digital pathology has significantly advanced cancer diagnosis by enabling high-resolution visualisation and assessment of tissue specimens. However, t...

NutriSighT: Interpretable Transformer Model for Dynamic Prediction of Hypocaloric Enteral Nutrition in Mechanically Ventilated Patients

Achieving adequate enteral nutrition among mechanically ventilated patients is challenging, yet critical. We developed NutriSighT, a transformer model...

A deep learning model for clinical outcome prediction using longitudinal inpatient electronic health records

Recent advances in deep learning show significant potential in analyzing continuous monitoring electronic health records (EHR) data for clinical outco...

Development and Validation of Machine Learning Models for Adverse Events after Cardiac Surgery

Early recognition of adverse events after cardiac surgery is vital for treatment. However, the widely used Society of Thoracic Surgery (STS) risk mode...

Generative AI Mitigates Representation Bias and Improves Model Fairness Through Synthetic Health Data

Representation bias in health data can lead to unfair decisions and compromise the generalisability of research findings. As a consequence, underrepre...

Machine Learning Models for Dynamic Assessment of Extubation Readiness in Pediatric Critical Care

Determining the optimal timing for extubation in critically ill children remains challenging, with premature extubation leading to increased morbidity...

Integrating Nowcasts into an Ensemble of Data-Driven Forecasting Models for SARI Hospitalizations in Germany

Predictive epidemic modeling can enhance situational awareness during emerging and seasonal outbreaks and has received increasing interest in recent y...