Critical Care

Authors reply: "A risk-adjusted length of stay to evaluate severe community-acquired pneumonia (sCAP) outcomes: A machine learning analysis of 16,985 ICU admissions".

Hybridized artificial intelligence system for reducing neonatal mortality in Nigeria.

BACKGROUND: Neonatal diseases represent the leading cause of death in Nigeria, ranking the country second globally in neonatal mortality rates. Early and accurate diagnosis remains challenging, leading to delayed interventions and increased mortality. AIM: To develop an artificial intelligence system capable of detecting multiple neonatal diseases using local datasets and advanced machine learning...

Explicit semantic guided bi-incomplete multi-modal hashing with label co-occurrence and label graph constraints.

Multi-modal hashing offers advantages in large-scale multimedia retrieval by integrating multi-modal features and generating compact binary codes for ...

Artificial intelligence-based models for clinical prognosis at the end of life in the ICU.

INTRODUCTION: Artificial intelligence (AI) has emerged as a promising tool to enhance clinical prediction in critically ill patients near the end of l...

Machine Learning-Enhanced Clinical Decision Support for Diagnosing Sinusitis With Nasal Endoscopy.

BACKGROUND: Sinusitis is a prevalent disease for which nasal endoscopy (NE) is an optimal diagnostic modality. However, NE accuracy is limited by inte...

Hospital-acquired pneumonia: Past, present, and perspectives.

Hospital-acquired pneumonia (HAP) remains the most frequent and lethal hospital acquired infection, driving ICU mortality, prolonged length of stay, a...

Artificial intelligence in ECMO management.

Sysmex Cell Population Data for Diagnosing Infection in Patients With Suspected Sepsis in the Emergency Department.

OBJECTIVES: Early diagnosis of suspected sepsis is crucial to improve patient survival. Cell population (CP) data, a set of leucocyte research paramet...

Multi-view spectral clustering algorithm based on bipartite graph and multi-feature similarity fusion.

Multi-view clustering remains a challenging task due to the heterogeneity and inconsistency across multiple views. Most esisting multi-view spectral c...

Interpretable artificial intelligence to predict preoperative risk factors for failure to rescue after coronary artery bypass grafting.

OBJECTIVE: Failure to rescue (FTR) is a significant quality indicator for postoperative cardiothoracic care. We developed an interpretable artificial ...

Progressive multi-branch video style transfer network via confidence reweighted projection.

Achieving temporal consistency in video content poses a significant challenge for high-quality video styling. Unfortunately, current video style trans...

Ahnak and Nckap1l as potential diagnostic biomarkers and therapeutic targets in Landiolol-mediated sepsis treatment.

PURPOSE: Landiolol is a beta-blocker used in the treatment of Sepsis. However, how this drug influences key genes and pathways involved in disease rem...

A Deep Learning-Based EffConvNeXt Model for Automatic Classification of Cystic Bronchiectasis: An Explainable AI Approach.

Cystic bronchiectasis and pneumonia are respiratory conditions that significantly impact morbidity and mortality worldwide. Diagnosing these diseases ...

AI in critical care: A roadmap to the future.

Artificial intelligence (AI) has the potential to revolutionize critical care medicine by enhancing patient care, improving resource allocation and re...

A New Method of Modeling the Multi-stage Decision-Making Process of CRT Using Machine Learning with Uncertainty Quantification.

Current machine learning-based (ML) models usually attempt to utilize all available patient data to predict patient outcomes while ignoring the associ...

Deep learning-based cross-device standardization of surface-enhanced Raman spectroscopy for enhanced bacterial recognition.

Surface-enhanced Raman spectroscopy (SERS) is a powerful, label-free technique for pathogen detection; however, its broader adoption in clinical diagn...

From promise to practice: A roadmap for artificial intelligence in critical care.

Artificial intelligence (AI) has regained strong momentum in medicine, driven by unprecedented computing power and the availability of massive clinica...

AI-powered insights in pediatric nephrology: current applications and future opportunities.

Artificial intelligence (AI) is rapidly emerging as a transformative force in pediatric nephrology, enabling improvements in diagnostic accuracy, ther...

Tacit mechanism: Bridging pre-training of individuality to multi-agent adversarial coordination.

To tackle the multi-agent adversarial coordination problem, current multi-agent reinforcement learning (MARL) algorithms primarily depend on team-base...

Toward Standardized ICU Trauma Resuscitation via Markov Decision Processes.

OBJECTIVE: Guideline-based recommendations for posthemostasis resuscitation in trauma patients remain limited. This study aimed to define an interpret...