Intensivists

Developing an ICU Mortality Risk Prediction Model for Acute Myocardial Infarction Patients Based on Machine Learning

Patients with acute myocardial infarction (AMI) are in a critical condition, facing a high risk of death in the intensive care unit (ICU) with significant individual differences. The aim of this study is to integrate clinical data using machine learning algorithms to construct a model for predicting the risk of death in ICU for AMI patients, thereby providing clinicians with an objective risk asse...

CardiacGPT™: A Real-Time AI Assistant for Intraoperative Guidance and Postoperative Decision Support in Cardiac Surgery

Cardiac surgery is one of the most complex and high-stakes areas of medicine, where intraoperative decisions must be made within seconds and incomplete information can compromise outcomes. Traditional risk scores and rule-based decision support tools provide limited real-time guidance and rarely integrate the unstructured data streams available during surgery. Recent advances in large language mod...

Predicting ICU Transfer and Short-term Mortality in Emergency Department Atrial Fibrillation Patients: An Enhanced Machine Learning Model Using MIMIC Data

Atrial fibrillation (AF) is a prevalent condition in emergency department (ED) patients and is associated with an elevated risk of intensive care unit...

Predicting Rejection Risk in Heart Transplantation: An Integrated Clinical–Histopathologic Framework for Personalized Post-Transplant Care

Cardiac allograft rejection (CAR) remains the leading cause of early graft failure after heart transplantation (HT). Current diagnostics, including hi...

Scalable screening for emergency department missed opportunities for diagnosis using sequential eTriggers and large language models

Missed opportunities for diagnosis (MODs), sometimes termed diagnostic errors, are a major cause of patient morbidity and mortality in the emergency d...

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 morbidity and mortality. AKI lasting over 48 hours, known ...

Multi-View Echocardiographic Embedding for Accessible AI Development

Echocardiography serves as a cornerstone of cardiovascular diagnostics through multiple standardized imaging views. While recent AI foundation models ...

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 can cause fluid overload. Restrictive fluid strat...

Admission photoplethysmography-based mortality prediction in hospitalized Ugandan children with suspected or confirmed infection: a feasibility study

Sepsis remains a major cause of preventable pediatric hospital deaths in developing countries, with progress hindered by the lack of effective risk id...

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 therapeutic strategies and inform resource allocat...

Metformin use is associated with lower mortality from bacterial sepsis and improved immunocompetence in Thai diabetes patients with acute melioidosis

Diabetes mellitus (DM) is a major risk factor for acquiring infections. Metformin, the first-line treatment for type 2 DM, is associated with benefici...

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. We aimed to identify distinct lymphocyte recovery...

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 failure within 48 hours of hospital admission in patient...

Developing an Early Diagnostic Signature and Deciphering the Microbial-Host Dynamics in Lower Respiratory Tract Infection (LRTI) in Paediatric Intensive Care Unit (PICU) Patients

Lower respiratory tract infection (LRTI) is a leading cause of morbidity and mortality among children admitted to paediatric intensive care units (PIC...

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 limited therapeutic options, increased morbidity, and e...

Analyzing Information Disparities across Modalities in Mortality Prediction

Recent advances in deep learning have enabled the integration of heterogeneous data modalities for clinical prediction, allowing models to exploit com...

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 timely intervention and improved outcomes. Artificial i...

From free text to SOFA score: automated reconstruction of sepsis severity from unstructured clinical notes

To evaluate the ability of a natural language processing system to automatically reconstruct the SOFA score from unstructured clinical notes in patien...

Predicting Disease Progression in Critically Ill Patients Using Frequency-Enhanced Time-series Forecasting

Accurate disease progression prediction is vital for managing critically ill patients in intensive care. Existing deep learning approaches mainly oper...

Machine Learning Prediction of Blood Pressure Control in Patients With Hypertension and Heart Failure Using Longitudinal Clinical Data

To develop and validate machine learning models for predicting Blood Pressure (BP) control status using demographic characteristics and longitudinal B...