Critical Care

Predictive Modeling of Acute Respiratory Distress Syndrome Using Machine Learning: Systematic Review and Meta-Analysis.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a critical condition commonly encountered in the intensive care unit (ICU), characterized by a high incidence and substantial mortality rate. Early detection and accurate prediction of ARDS can significantly improve patient outcomes. While machine learning (ML) models are increasingly being used for ARDS prediction, there is a lack of conse...

Rapid Response System Restructure: Focus on Prevention and Early Intervention.

This article describes the staged restructure of the rapid response program into a dedicated 24/7 proactive rapid response system in a quaternary academic medical center in the southern United States. Rapid response nurses (RRNs) completed clinical leadership training on artificial intelligence, electronic risk stratification alerts, expert nurse rounding, emergency response, teamwork, closed-loop...

[Chest radiological lesions in COVID-19 : from classical imaging to artificial intelligence].

In the course of the pandemic induced by the appearance of a new coronavirus (SARS-CoV-2; COVID-19) causing acute respiratory distress syndrome (ARDS)...

Paradigm-Shifting Attention-based Hybrid View Learning for Enhanced Mammography Breast Cancer Classification with Multi-Scale and Multi-View Fusion.

Breast cancer poses a serious threat to women's health, and its early detection is crucial for enhancing patient survival rates. While deep learning h...

GAMMNet: Gating Multi-head Attention in a Multi-modal Deep Network for Sound Based Respiratory Disease Detection.

Respiratory diseases present significant challenges to global health due to their high morbidity and mortality rates. Traditional diagnostic methods, ...

Application of Metaheuristics for Optimizing Predictive Models in iHealth: A Case Study on Hypotension Prediction in Dialysis Patients.

Intradialytic hypotension (IDH) is a critical complication in patients with chronic kidney disease undergoing dialysis, affecting both patient safety ...

Development, Validation, and Deployment of a Time-Dependent Machine Learning Model for Predicting One-Year Mortality Risk in Critically Ill Patients with Heart Failure.

Heart failure (HF) ranks among the foremost causes of mortality globally, exhibiting particularly high prevalence and significant impact within inten...

Mitigating Bias in Machine Learning Models with Ethics-Based Initiatives: The Case of Sepsis.

This paper discusses ethics-based strategies for mitigating bias in machine learning models used to predict sepsis onset. The first part discusses how...

Investigation of Bleeding Disorders: When and How Should We Test Platelet Functions?

Inherited platelet disorders (IPDs) are rare conditions with diverse underlying pathophysiology which should be suspected in patients presenting with ...

Overview of the NLPCC 2025 Shared Task 4: Multi-modal, Multilingual, and Multi-hop Medical Instructional Video Question Answering Challenge

Following the successful hosts of the 1-st (NLPCC 2023 Foshan) CMIVQA and the 2-rd (NLPCC 2024 Hangzhou) MMIVQA challenges, this year, a new task ha...

A scalable deep attention mechanism of instance segmentation for the investigation of chromosome.

Chromosome segmentation in metaphase images is a critical yet challenging task in cytogenetics and genomics due to the inherent complexity, variabilit...

Reproducing and Improving CheXNet: Deep Learning for Chest X-ray Disease Classification

Deep learning for radiologic image analysis is a rapidly growing field in biomedical research and is likely to become a standard practice in modern ...

A novel scheme for non-invasive drug delivery with a magnetically controlled drug delivering capsule endoscope.

There is a lack of effective means for precise drug delivery of gastrointestinal diseases. Herein we report a novel magnetically controlled drug deliv...

Machine learning model to predict sepsis in ICU patients with intracerebral hemorrhage.

Patients with intracerebral hemorrhage (ICH) are highly susceptible to sepsis. This study evaluates the efficacy of machine learning (ML) models in pr...

Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks

With the advancement of large language models and embodied Artificial Intelligence (AI) in the intelligent transportation scenarios, the combination...

Multi-Modal Molecular Representation Learning via Structure Awareness

Accurate extraction of molecular representations is a critical step in the drug discovery process. In recent years, significant progress has been ma...

Human-in-the-Loop AI for HVAC Management Enhancing Comfort and Energy Efficiency

Heating, Ventilation, and Air Conditioning (HVAC) systems account for approximately 38% of building energy consumption globally, making them one of ...

Clinical Bedside Benchmarking Test for Measuring the Total Hemoglobin Concentration.

OBJECTIVE: Accurate total hemoglobin concentration (ctHb) measurement is critical for clinical decision-making, particularly in acute care, where imme...

Thirty-day mortality risk prediction for geriatric patients undergoing non-cardiac surgery in the surgical intensive care unit.

BACKGROUND: The prediction of mortality for elderly patients undergoing non-cardiac surgeries is a vital research area, as accurate risk assessment ca...

Emerging use of pulmonary artery and cardiac pressure sensing technology in the management of worsening heart failure events.

Unplanned admissions for worsening heart failure (WHF) are the largest resource cost in heart failure (HF) management. Despite advances in pharmacolog...