Critical Care

Latest AI and machine learning research in critical care for healthcare professionals.

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Subcategories: Sepsis
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Personalized Hemodynamic Management Using Reinforcement Learning to Prevent Persistent Acute Kidney Injury After Cardiac Surgery

Acute kidney injury (AKI) affects one-third of patients after cardiac surgery and increases morbidit...

Using Hourly Aggregated Respiratory Rate and Expiratory Time with Machine Learning to Identify Remote COPD Exacerbations

Exacerbations of chronic obstructive pulmonary disease (COPD) are a major cause of morbidity and mor...

Multi-View Echocardiographic Embedding for Accessible AI Development

Echocardiography serves as a cornerstone of cardiovascular diagnostics through multiple standardized...

Personalized Fluid Management in Patients with Sepsis and AKI: A Casual Machine Learning Approach

Intravenous (IV) fluids are cornerstone for management of acute kidney injury (AKI) after sepsis but...

Proteomic and clinical impact of human knockouts in British South Asians

Human loss-of-function (LoF) variants affecting both copies of a gene (“human knockouts”) provide a ...

Development and Validation of a parsimonious AI-Based Risk Score for Mortality in Heart Failure: A UK cohort study

Accurate risk stratification in heart failure (HF) is crucial to guide clinical decisions, optimise ...

Explainable machine learning on weighted connectivity networks across frequencies for outcome prediction in comatose patients

Accurate early prediction of neurological outcomes in comatose patients after cardiac arrest is crit...

Dynamic Lymphocyte Recovery Patterns Predict 90-Day Mortality in Sepsis: A Machine Learning-Enhanced Analysis of the MIMIC-IV Cohort

Sepsis-induced immunosuppression, characterized by lymphopenia, is associated with adverse outcomes....

Development of a Multi-Model Ensemble Tool for Early Prediction of 48-Hour Respiratory Failure Risk in CAP Patients

To develop a predictive tool capable of early identification of the risk of acute respiratory failur...

LLM-based Multi-Agent Collaboration for Abstract Screening towards Automated Systematic Reviews

Systematic reviews (SRs) are essential for evidence-based practice but remain labor-intensive, espec...

Predicting Carbapenem Resistance in Hospitalized Patients Using Machine Learning: A Retrospective Analysis of the MIMIC-III Database

Carbapenem-resistant Gram-negative bacteria (CR-GNB) represent a major health challenge due to limit...

Analyzing Information Disparities across Modalities in Mortality Prediction

Recent advances in deep learning have enabled the integration of heterogeneous data modalities for c...

Development of Alzheimer’s Disease Risk Score for Future Primary Care: A White-Box Approach

Interpretable scoring system can contribute to bridge the gap between the timeliness and complexity ...

Continuous Multimodal AI with Wearable Vital Signs Predicts Postoperative Complications in the General Ward

Surgery is inherently associated with complications, making early detection the cornerstone of timel...

Precision Immunosuppression and Long-Term Kidney Transplant Outcomes: A Dual Survival Modeling Framework

Optimizing immunosuppressive therapy remains central to improving long-term outcomes after kidney tr...

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