Critical Care

Understanding HFpEF Through Imaging: Strengths, Limitations, and Current State of the Art.

Heart failure with preserved ejection fraction (HFpEF) is a clinical syndrome characterized by dyspnea caused by hemodynamic congestion, which develops at rest or with exercise. Noninvasive diagnosis remains challenging, because resting diastolic pressures may be normal, and even when abnormal, currently applied noninvasive indices lack sensitivity. The pathophysiology of HFpEF extends far beyond ...

Machine Learning-Augmented Traditional Analysis of Lactate vs Lactate-to-Albumin Ratio for Predicting Mortality Risk in Patients With Sepsis: Large-Scale Retrospective Study.

BACKGROUND: Effective risk stratification in sepsis remains a critical clinical challenge. Serum lactate is a cornerstone biomarker of metabolic dysfunction, yet its predictive limitations-particularly in patients without severe hyperlactatemia-are well recognized. The lactate-to-albumin ratio (LAR), a composite mixed-unit index integrating markers of acute metabolic dysfunction and systemic infla...

Physics-Informed LSTM Neural Network for Personalized Pharmacokinetic Modeling of Polymyxin B in Critically Ill Patients.

Polymyxin B represents a critical last-resort antibiotic for carbapenem-resistant Gram-negative infections, yet its narrow therapeutic window and subs...

A Multimodal Deep Learning Model for Preoperative Prediction of Postoperative Complications in Gastric Cancer.

BACKGROUND: Clavien-Dindo (CD) grade ≥II complications occur in roughly one in five patients after curative gastrectomy for gastric cancer and indepen...

Acute kidney injury severity in ICU patients: Developing and evaluating a data-driven analysis of clinical covariates using machine learning.

BACKGROUND: Acute kidney injury remains a major cause of morbidity and mortality in critically ill patients. Existing classification systems rely on s...

Simulator development using natural language: clinician-led innovation through artificial intelligence.

BACKGROUND: Healthcare simulation training faces significant barriers due to the "clinician-developer gap," where educators lack programming expertise...

Machine Learning-Based Early Risk Stratification for Sepsis-Related Troponin-Defined Myocardial Injury Using Routine Metabolic Indicators.

BACKGROUND: Early recognition of sepsis-related myocardial injury during sepsis remains difficult, partly because harmonized echocardiographic phenoty...

Leashing heterogeneous sepsis with phenotype-informed decision support modeling.

Detection of bronchopulmonary dysplasia in infants and prediction of school-age lung function from tidal breathing data using recurrent neural networks.

Objective.To test whether machine learning (ML) models trained on tidal breathing flow time series can discriminate between individuals with and witho...

Biomarkers in predicting mortality in hemodialysis patients: recent advances.

Patients undergoing maintenance hemodialysis face annual mortality rates of 15-27%, with cardiovascular causes accounting for more than half of all de...

Non-invasive arterial blood pressure waveform generation in critically ill patients: A sensor-based deep learning approach.

Continuous monitoring of Arterial Blood Pressure (ABP) in critically ill patients requires invasive arterial catheterization, which carries risks of t...

Optimizing stroke mortality prediction: a comparative study of sampling, cost-sensitive learning, and threshold adjustment.

BACKGROUND: Stroke is a leading cause of mortality and disability worldwide, creating a critical need for accurate prediction tools to support early c...

An Interactive Virtual Nephrologist to Engage Patients With Advanced CKD About Transplantation.

RATIONALE & OBJECTIVE: Despite advantages in survival and quality of life with kidney transplantation (KT) compared to other treatments for kidney fai...

An interpretable machine learning model for early prediction of ESBL-producing bacteraemia in the emergency department.

Empirical antibiotic therapy in the emergency department (ED) is often initiated before susceptibility results are available, risking treatment failur...

Fairness in multimodal machine learning applications in clinical decision support: a systematic review.

Multimodal AI models are increasingly used in clinical decision support systems, but fairness appears to be rarely measured. In this systematic review...

Open dataset and deep learning model for intelligent diagnosis of neonatal respiratory distress syndrome and aspiration syndrome in newborns.

Neonatal pulmonary diseases such as aspiration syndrome (AS) and respiratory distress syndrome (NRDS) require timely diagnosis, yet manual interpretat...

Association Between the Hemoglobin-to-Red Blood Cell Distribution Width Ratio and Perioperative Mortality Risk in Myasthenia Gravis Patients with Sepsis: A Retrospective Cohort Study.

BackgroundThis study aims to explore the association between the Hemoglobin/Red Cell Distribution Width Ratio (HRR) and perioperative mortality in mya...

Deep learning-based respiratory-triggered magnetic resonance cholangiopancreatography: comparison with conventional respiratory-triggered and breath-hold magnetic resonance cholangiopancreatography.

PURPOSE: To evaluate whether deep learning-based respiratory-triggered (DL) 3D magnetic resonance cholangiopancreatography (MRCP) improves acquisition...

Predicting Neonatal Respiratory Outcomes Using Machine Learning: A Systematic Review Through a Life Course Lens.

BACKGROUND: Neonatal respiratory outcomes remain leading drivers of neonatal intensive care unit (NICU) morbidity, mortality, and prolonged hospitaliz...

AI-Assisted Chest X-Ray Interpretation in Resource-Limited Settings: LuAna Stepped-Wedge Trial Protocol.

BACKGROUND: Artificial intelligence (AI) has the potential to transform chest radiography interpretation by enhancing diagnostic accuracy, identifying...