Critical Care

Utility of E-Nose in Paediatric Respiratory Diseases Using Machine Learning Approach: A Pilot Study.

To assess the feasibility of an electronic nose (e-nose) for volatile organic compounds (VOCs) to classify respiratory diseases and establish a protocol for use. We analysed exhaled air samples with Cyranose 320 nose from 67 children (14 healthy, 34 asthmatics and 19 other respiratory diseases) (mean age 11.4 years). Three samples were collected for each patient. We used machine learning models to...

Quantitative Cerebrovascular Analysis for Improved Prediction of Post-Stroke Complications.

Endovascular thrombectomy (EVT) has transformed the treatment of acute ischemic stroke (AIS). However, a substantial proportion of AIS patients experience poor outcomes despite successful recanalization, often due to severe neurological deterioration or life-threatening complications. Early identification of these high-risk patients remains a major unmet need. In this study, we developed and valid...

Feasibility of Tailoring Artificial Intelligence-Assisted Ambient Scribes for Intensive Care Unit Rounds: Algorithm Development and Validation.

BACKGROUND: The increasing documentation burden on physicians is a significant contributor to burnout and decreases in care quality. Artificial intell...

From single targets to circuits diversified discovery and development strategies towards new paradigms for non-opioid analgesics.

The long-term use of opioids for analgesia is associated with serious adverse effects such as addiction and respiratory depression, as well as a poten...

Development and multicenter validation of machine learning models for 28-day mortality in critically ill patients with acute exacerbation of chronic obstructive pulmonary disease.

BACKGROUND: Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is a common critical illness in the intensive care unit (ICU) and is ...

DeepKneeXR: YOLOv8 multi-label X-rays detection of knee abnormalities from sports injury with clinical explainability.

BACKGROUND: Soft-tissue knee abnormalities are common, yet first-line radiography provides limited soft-tissue contrast, whereas MRI or arthroscopy is...

An explainable machine learning model for predicting high phosphorus risk in patients on maintenance hemodialysis: a multicenter retrospective study.

BACKGROUND: Hyperphosphatemia is a common complication in hemodialysis and serves as a key marker for evaluating dialysis adequacy. This study aimed t...

Advances in Antiviral Drug Development Targeting Viral Proteases.

Viral proteases are critical targets in antiviral drug development due to their essential role in the viral lifecycle and high conservation across vir...

Improving Delirium Prediction in Surgery: Enhanced Machine Learning Models vs. Traditional Risk Scores.

BACKGROUND: Postoperative delirium (POD) is a prevalent complication among older surgical patients; however, accurately predicting its occurrence usin...

LKCAU-Net: A Large Kernel Coordinated Attention U-Net for Breast Tumors Segmentation in Ultrasound Images.

Breast cancer remains a significant health concern for women worldwide. Ultrasound imaging is widely adopted for screening due to its non-invasive and...

Forecasting the Dialysis Burden in Japan: Validation-Based Projections of Prevalence and Incidence Through 2050.

BACKGROUND: Japan has one of the highest dialysis prevalence rates worldwide and a shrinking, aging population. Whether dialysis burden has entered a ...

Identifying critical period of type 2 diabetes in Chinese population with leading molecules based on dynamic network biomarkers.

Diabetes has become a significant global public health challenge, and prediabetes (preD), as the earliest detectable stage of glucose metabolism disor...

A population-based retrospective machine learning study of COVID-19 severity using integrated clinical and viral genomic data in Jiangsu Province, China.

BACKGROUND: As coronavirus disease 2019 (COVID-19) has transitioned into an endemic phase characterized by sustained transmission and widespread hybri...

A multi-fidelity tabular prior-data fitted network model for accurate prediction and uncertainty quantification.

Accurate prediction of unknown labels from feature-label datasets using machine learning is critical for applications spanning drug discovery, disease...

Development and external validation of a dual-stage predictive model for self-reported physician-diagnosed chronic lung disease, asthma, and lung cancer in Chinese adults.

Chronic respiratory diseases represent a leading cause of global mortality, yet robust prediction tools integrating onset risk and long-term prognosis...

Association between glucose-to-platelet ratio and all-cause mortality in critically ill patients with acute myocardial infarction: a retrospective cohort study.

BACKGROUND: Acute myocardial infarction (AMI) in critically ill patients is associated with high mortality. The glucose-to-platelet ratio (GPR), deriv...

Leveraging data-driven risk-adapted surveillance strategies after disease progression in locally advanced rectal cancer.

BACKGROUND: Post-progression survival (PPS) is a critical endpoint in oncology, yet predictors of PPS and data-driven strategies for post-progression ...

Constructing an Artificial Intelligence-Driven Multilingual Medical Health Education Chatbot with Domain-Specific Medical Knowledge.

Language barriers remain a critical challenge in health care delivery, particularly for migrant and multicultural populations. This study presents the...

Exploring potential key genes and mitotic catastrophe-associated correlates in sepsis-induced acute respiratory distress syndrome using transcriptomics and experimental validation.

BACKGROUND: Sepsis-induced acute respiratory distress syndrome (ARDS) is a life-threatening form of acute diffuse lung injury caused by sepsis. Howeve...

Automated identification of clinically important Candida yeast species for microscopic images using self-supervised learning.

Opportunistic fungal infections are an escalating global challenge, impacting over a billion people worldwide, particularly immunocompromised individu...