Critical Care

Development and multicenter validation of a machine learning model for postoperative sepsis risk in critically Ill traumatic spinal injury patients.

OBJECTIVE: To develop and validate a machine learning model for postoperative sepsis in critically ill traumatic spinal injury (TSI) patients, a frequent and severe complication without dedicated predictive tools. METHODS: Model development used the MIMIC-IV 3.1 database, with external validation in the eICU-CRD 2.0 database and a Chinese TSI cohort. Variables documented within 24 h of postoperati...

INTELLI-PVA: Informative sample annotation-based contrastive active learning for cross-domain patient-ventilator asynchrony detection.

BACKGROUND AND OBJECTIVE: Patient-ventilator asynchrony (PVA) is prevalent in mechanically ventilated patients and adversely impacts clinical outcomes, but its real-time detection remains challenging. While artificial intelligence (AI) systems show promise for PVA detection, their cross-domain generalization faces two major limitations: variability in patient-ventilator interactions across differe...

A novel hybrid sensor array with enhanced sensitivity and selectivity for biomarker detection of multiple respiratory diseases.

Electronic nose is an emergent technique for noninvasive disease detection via breath analysis, which is, however limited by the sensitivity and selec...

Development and validation of a novel machine learning-based algorithm to predict incident atrial fibrillation: A multicohort analysis.

BACKGROUND: Existing atrial fibrillation (AF) risk prediction models incorporate race as a covariate, systematically underestimating AF risk in black ...

Pharmacoepidemiologic characterization of cannabis use and symptomatology in rheumatology using natural language processing of electronic health record clinic notes.

Up to 70% of patients with autoimmune rheumatic diseases (ARDs), including rheumatoid arthritis, psoriatic arthritis, and systemic lupus erythematosus...

Benchmarking foundation models and parameter-efficient fine-tuning for prognosis prediction in medical imaging.

BACKGROUND AND OBJECTIVES: Despite the significant potential of Foundation Models (FMs) in medical imaging, their application to prognosis prediction ...

Assessing the Readiness of Health Care Organizations for Safe AI Integration: Perspectives From Quality and Safety Leaders.

BACKGROUND: Artificial intelligence (AI) technologies hold great promise for improving patient outcomes, reducing clinician workload, and enhancing pa...

Artificial Intelligence and Machine Learning in Health Care: Current Capabilities, Emerging Potential, and Future Directions.

This article reviews how artificial intelligence and machine learning are reshaping health care. After a historical overview and glossary, we explore ...

Wildfire-sourced PM2.5 and cause-specific emergency department visits in Victoria, Australia from 2014 to 2019.

Wildfires-sourced (WS) fine particulate matter (PM2.5) is known to adversely impact human health, while evidence regarding the burden of emergency dep...

Prediction of respiratory rate from schlieren images using artificial intelligence.

BACKGROUND AND OBJECTIVE: Respiratory rate is a fundamental physiological parameter and one of the earliest indicators that plays a crucial role in as...

Machine Learning-Based Prediction of Postoperative Vasoplegia After Cardiac Surgery.

OBJECTIVE: To develop and internally validate a machine-learning model for the early prediction of postoperative vasoplegia after cardiac surgery. DES...

Pressure injury surveillance in the intensive care unit: Development, validation, and clinical application of a natural language processing algorithm.

INTRODUCTION: Pressure injuries (PIs) are one of the most common hospital-acquired complications in patients admitted to an intensive care unit (ICU)....

Development and validation of deep continual learning model to sequentially learn multiple clinical prediction tasks for ICU patients.

BACKGROUND: ICU patients often suffer from critical and complex condition, and multiple potential risks should be monitored to provide them comprehens...

Prospective Respiratory Motion Correction Using Machine Learning and Pilot Tone (PROMPT) in Cardiac MRI.

PURPOSE: To implement and evaluate the feasibility of a Pilot Tone (PT)-based prospective gating and tracking technique, which uses a long short-term ...

Multi-label classification for multi-temporal, multi-spatial coral reef condition monitoring using vision foundation model with adapter learning.

Coral reef ecosystems provide essential ecosystem services, but face significant threats from climate change and human activities. Although advances i...

Occupational Exposure to Welding Fumes Associated With Pulmonary Surfactant Lipid Compositional Changes, Inflammatory Activity, and Pulmonary Functioning.

OBJECTIVE: This study aimed to investigate alterations in the respiratory tract lining fluid phospholipids and their association with pulmonary functi...

Artificial intelligence-guided nutritional therapy in the ICU.

PURPOSE OF REVIEW: Critical care nutrition remains a high-stakes and error-prone domain, particularly given the complex metabolic demands and heteroge...

Development and validation of a machine learning model to predict 30-day mortality in ischemic stroke patients with consciousness impairment: Insights from MIMIC-IV database and multicenter ICU data in China.

BACKGROUND: Patients with ischemic stroke complicated by consciousness disorders remain associated with high mortality risks. This study aims to devel...

Extreme cardiac MRI analysis under respiratory motion: Results of the CMRxMotion challenge.

Deep learning models have achieved state-of-the-art performance in automated Cardiac Magnetic Resonance (CMR) analysis. However, the efficacy of these...

Nationwide longitudinal evaluation of a machine learning approach for enhanced interpretation of Xpert MTB/RIF ultra rifampicin-resistance results in low bacterial load tuberculosis specimens.

BACKGROUND: The World Health Organization (WHO) has identified tuberculosis (TB) as the leading cause of death from a single infectious agent. False-p...