Critical Care

Predicting surgical intensive care unit readmission with machine learning model: Bi-center training and validation.

BACKGROUND: Patients readmitted to the surgical intensive care unit (SICU) face a high risk of morta...

Automated Detection of Ventilator Asynchronies: A Clinical and Technological Perspective.

Patient-ventilator asynchrony is highly prevalent during invasive mechanical ventilation, yet its de...

Building an Extracorporeal Membrane Oxygenation Digital Twin Using High-Resolution Patient Data: An artificial intelligence model for virtual reality simulation.

OBJECTIVES: Extracorporeal membrane oxygenation (ECMO) is a life-saving therapy for severe cardiopul...

Predicting cardiopulmonary exercise testing outcomes in congenital heart disease through multimodal data integration and geometric learning.

Cardiopulmonary exercise testing (CPET) provides a comprehensive assessment of functional capacity b...

Development and Validation of a Frailty Risk Prediction Model for Preoperative Non-Small-Cell Lung Cancer Patients: A Cross-Sectional Study.

PURPOSE: Frailty is increasingly recognized as a predictor of poor surgical outcomes, yet its preope...

Vulnerability and short clinical outcomes in patients on the deceased-donor kidney transplant waiting list.

BACKGROUND: With prolonged waiting times for deceased-donor kidney transplantation (DDKT) in Japan, ...

Deep Learning-Based Auto-Navigation for Free-Breathing Golden-Angle Radial MRI.

PURPOSE: To develop a deep learning-based auto-navigation technique for free-breathing golden-angle ...

Interpretable machine learning and deep neural networks for ICU admission prediction in paediatric respiratory patients.

Direct Integration of Ionic Liquid Gel Sensors onto Microfibrous Face Mask Substrates for Wearable Respiratory Health Monitoring.

Mask-integrated respiratory sensors are promising for noninvasive monitoring of respiratory health o...

Sepsis Diagnostics via Biosensors: Engineering Platforms, Artificial Intelligence Integration, and Clinical Translation.

Sepsis remains one of the most diagnostically challenging syndromes due to its clinical heterogeneit...

Clinical validation of perfusion imaging with pulmonary function test data using Voronoi-based discretization.

Accurate lung function assessment is essential for diagnosing and managing diseases like COPD, pulmo...

Estimating the odds ratio from the output scores of machine learning models: possibilities and limitations.

Estimation of exposure-response association is central to epidemiologic research. Although the advan...

Fuzzy logic in respiratory medicine: a systematic review of predictive and diagnostic applications.

BACKGROUND: There is increasing interest in the use of artificial intelligence (AI) to assist with r...

Artificial intelligence and machine learning applications in dialysis: Current applications, challenges, and future directions.

Artificial intelligence (AI) and machine learning (ML) applications have emerged as transformative t...

In-hospital testing of NIVPredict - an AI tool for early prediction of non-invasive ventilation outcome in acute respiratory failure.

Personalized sepsis mortality prediction: An interpretable machine learning nomogram.

PURPOSE: Sepsis remains a major cause of mortality in ICU patients, requiring accurate prognostic to...

Development and external validation of a machine learning model for predicting in-hospital mortality in acute liver failure.

BACKGROUND: Acute liver failure (ALF) is a rapidly progressive and life-threatening condition that r...

A Machine Learning-Based Prognostic Model for Sepsis-Associated Liver Injury Using Routine Indicators.

OBJECTIVE: Sepsis-associated liver injury (SALI) occurs in approximately 40% of sepsis cases and is ...

A machine learning model to identify pulmonary embolism in patients admitted to intensive care.

BACKGROUND: Pulmonary embolism (PE) is a leading cause of preventable death, yet statistical predict...

Beyond handcrafted radiomics in oncologic imaging: Innovations in deep, explainable, multi-site and multi-omics radiomics approaches.

Radiomics seeks to convert medical images into quantitative biomarkers capable of capturing tumor ph...

Interpretable four-factor day-1 nomogram for predicting sepsis-associated encephalopathy in septic ICU patients with AKI: Development and internal validation in MIMIC-IV.

Sepsis-associated encephalopathy (SAE) is common in the intensive care unit (ICU) and portends worse...