Critical Care

DIVE: A Multi-Label Smart Contract Vulnerability Dataset.

Smart Contract (SC) vulnerabilities are programming errors or design flaws that can lead to financial loss or functional failure, making accurate detection essential. Although Machine Learning (ML) is widely applied to SC vulnerability detection, existing datasets are often small, imbalanced, inconsistently labeled, or nonstandardized, and frequently rely on limited feature representations that do...

A deep learning framework for breast cancer diagnosis using Swin Transformer and Dual-Attention Multi-scale Fusion Network.

Breast cancer is among the most prevalent cancers affecting women worldwide, and early detection through mammography is critical to reducing mortality rates. Convolutional neural networks (CNNs) have demonstrated notable effectiveness in classifying mammograms. However, they are constrained in their ability to capture long-range contextual dependencies. On the other hand, transformer-based models ...

Detecting multimorbidity patterns in Alzheimer's disease using unsupervised machine learning: A nationwide emergency department study (2007-2022).

BackgroundAlzheimer's disease (AD) patients frequently present to emergency departments (EDs) with complex comorbidities that complicate triage and ma...

[Annual progress in critical care of pulmonary medicine 2025].

This review systematically summarizes the annual research advances in the field of critical care of pulmonary, with a focus on pulmonary and critical ...

Modern machine learning techniques used in prediction of sepsis and bloodstream infection in burn patients: A systematic review.

INTRODUCTION: Burn patients are a group highly prone to sepsis and bloodstream infections (BSIs) due to immune dysregulation, skin barrier loss, and c...

Community medical centers struggle to produce well-calibrated clinical prediction models: Data augmentation can help.

OBJECTIVE: Machine learning models (ML) often require localization to perform optimally in local populations. We hypothesize that smaller community he...

Deep learning-based 4D robust optimization of intensity-modulated proton therapy for lung cancer radiotherapy.

Intensity-modulated proton therapy (IMPT) provides steep dose gradients but is vulnerable to range uncertainties and respiratory motion, leading to in...

A machine learning-based model to predict multi-time-point prognosis for acute-on-chronic hepatitis B liver failure.

Our prognostic model and mobile application enable multi-time-point prognostic evaluation for patients with acute-on-chronic hepatitis B liver failure...

Bridging Population Patterns and Individual Prediction: Framework for Prospective Multimorbidity Study.

BACKGROUND: Multimorbidity has become a major global public health challenge. However, existing research primarily emphasizes the identification of di...

Identifying and timing patient outcomes in clinician notes using large language models.

BACKGROUND: Key challenges in leveraging unstructured clinician notes for predictive models include identifying and timing patient outcomes. To addres...

SwinPix: A Swin Transformer-based Pix2Pix Framework for Low-Dose PET Denoising Using Multi-level Inputs Toward Standard-Dose Quality.

This study introduces SwinPix, a novel network architecture designed to explore the effectiveness of multi-level low-dose (LD) PET inputs as prior kno...

Resveratrol ameliorates sepsis-induced immune dysfunction by targeting GADD45A to maintain hematopoietic stem cell homeostasis: A predictive study integrating ensemble machine learning, single-cell transcriptomics, and molecular dynamics simulation.

OBJECTIVE: To investigate the potential pharmacological mechanisms of resveratrol (RES) in ameliorating sepsis-associated immune dysfunction via the G...

Machine learning-based prognostic analysis of patients with status epilepticus in the neurological intensive care unit.

OBJECTIVE: This study aimed to develop and validate machine learning (ML) models for predicting the prognosis of status epilepticus (SE) patients with...

Precise oxygen therapy to emphysema patients by fuzzy-based gain tuning control of set-point regulated MRAC.

Emphysema, a primary component of chronic obstructive pulmonary disease (COPD), causes progressive dyspnea through the destruction of alveolar membran...

Artificial Intelligence-Based Pathological Subtype Diagnosis of Nasal Polyps: A Multidimensional and Micro-Visualization Study.

BACKGROUND: Nasal polyps (NP) are common upper respiratory conditions with diverse inflammatory subtypes influencing clinical features and prognosis. ...

Preoperative Risk Score for Mortality Within 3 Years of Elective Aortobifemoral Artery Bypass.

BACKGROUND: The purpose of this study was to create a risk score for mortality within 3 years of elective aortobifemoral artery bypass for aortoiliac ...

Machine learning prediction model for delirium after heart valve replacement with cardiopulmonary bypass: A large-scale cohort study.

OBJECTIVE: Postoperative delirium (POD) is a common and severe complication following heart valve replacement (HVR) with cardiopulmonary bypass (CPB),...

A 1D Snoring Waveform and 2D Composite Acoustic Feature Graph-Based Multi-Modal Fusion Network for Obstructive Sites Recognition.

As a critical factor in diagnostic work-up and treatment decision-making process of sleep-related breathing disorders, accurate localization of obstru...

The relationship between urinary tract infections and sepsis: results from Mendelian randomization and MIMIC data study.

Respiratory sound analysis for ICU clinical decision support: deep learning-based classification of normal and abnormal sounds using real ICU data.