Critical Care

Clinical Applications of Data Science and Machine Learning in the Pediatric Cardiac Intensive Care Unit.

To synthesize and critically appraise applications of machine learning (ML) in pediatric cardiac int...

A novel approach to preeclampsia early prediction addressing predictive uncertainty due to missing data in clinical dataset.

Preeclampsia (PE) is a disease that seriously threatens the health of pregnant women, and early inte...

[Application of a novel robot-assisted navigation system in CT-guided percutaneous lung biopsy].

Objective: To compare the clinical efficacy and safety of robot-assisted navigation systems with tho...

Leveraging natural language processing and machine learning to identify chronic conditions from primary care electronic medical records.

Primary care electronic medical records (EMRs) contain rich data that can support proactive identifi...

Machine learning-based on model for explain risk of 24-hour death in critically ill patients in the prehospital setting: A retrospective cohort study.

This study aimed to develop and validate a machine learning-based model for predicting 24-hour morta...

Artificial intelligence and predictive analytics in obstetric anesthesia: early warning for maternal complications.

PURPOSE OF REVIEW: Maternal morbidity and mortality remain largely preventable, yet current risk-ass...

Machine learning for hemodynamic instability prediction and hemorrhage management in trauma and perioperative care.

PURPOSE OF REVIEW: Hemodynamic instability and uncontrolled hemorrhage remain leading causes of prev...

A Large Language Model Approach to Functional Status Scale Assessment.

OBJECTIVES: To develop a fine-tuned version of the generative pretrained transformer (GPT)-4o artifi...

Artificial Intelligence-Assisted Identification and Prevention of Chronic Obstructive Pulmonary Disease Using Biodegradable and Reusable Piezoelectric Elastomer/Poly(l-lactic acid) Composite Nanofiber Membrane.

Chronic obstructive pulmonary disease (COPD) is a respiratory disease imposing significant economic ...

Stress Detection Using Heart Rate Variability and Respiratory Signals Derived From a Single-Lead ECG.

Stress detection is a widely studied field due to its significant implications for mental and physic...

Practical machine learning model for early and accurate prediction of disseminated intravascular coagulation before its progression to an overt stage.

BACKGROUND AND AIMS: In patients with sepsis, anticoagulant therapy is expected to have maximal effi...

Respiratory physiology after resupination following prone ventilation to predict 28-day mortality in mechanically ventilated patients: a machine learning analysis.

K-CC-MoCo: A Fast k-Space-Based Respiratory Motion Correction for Highly Accelerated First-Pass Perfusion Cardiovascular MR.

PURPOSE: First-pass perfusion cardiovascular MR (FPP-CMR) enables the non-invasive diagnosis of micr...

Artificial intelligence-driven predictive analytics for postoperative management and recovery in trauma patients.

PURPOSE OF REVIEW: Post-traumatic care is evolving from a reactive, protocol-driven paradigm to a pr...

Exfoliated kidney cells from urine for non-invasive kidney transplant monitoring: A potential opportunity?

Kidney transplantation is usually the optimal treatment option for patients living with kidney failu...

Cardiovascular Risk Assessment Tools in Chronic Kidney Disease.

Cardiovascular (CV) risk calculators estimate the likelihood of CV events by integrating factors suc...

Carboxymethyl Cellulose/Sodium Lignosulfonate Composite Hydrogel with Toughness, Adhesiveness, and Antibacterial Properties for Monitoring Respiration in Heart Failure Patients.

The advancement of personalized medicine is increasingly reliant on wearable health monitoring techn...

Expert-level probabilistic breathing event detector informs phenotyping of sleep apnea.

Diagnosing sleep disordered breathing requires manual annotation of events from sleep studies, such ...

Plasma metabolomic signatures in patients with multidrug-resistant bacterial sepsis.

BACKGROUND AND OBJECTIVE: Multidrug-resistant (MDR) bacterial infections are a leading cause of seps...

Prediction of Adverse Events in Single Ventricle Physiology Infants Using Artificial Intelligence Tools.

BACKGROUND: Adverse events (AEs) within the cardiac ICU (CICU) are associated with high mortality an...

Utilising unsupervised machine learning to predict outbreaks of respiratory tract infections in acute Irish hospitals (2016-2021).

OBJECTIVES: To apply unsupervised machine learning (ML) to predict outbreaks of respiratory tract in...