Critical Care

Development and validation of interpretable machine learning models for dynamic prediction of prognosis in acute pancreatitis complicated by acute kidney injury: A multicenter study.

OBJECTIVE: This study aims to develop and validate interpretable machine learning (ML) models to dynamically predict mortality risk among intensive care unit (ICU) patients diagnosed with acute pancreatitis complicated by acute kidney injury (AP-AKI). METHODS: The clinical data in the training set, including demographic characteristics, laboratory indicators, scoring systems, treatment modalities,...

Shared inflammation in takotsubo syndrome and sepsis: Lipocalin-2 mediates neutrophil infiltration and cardiac damage.

BACKGROUND: Takotsubo syndrome (TTS) and sepsis often co-occur with poor outcomes, yet their underlying molecular mechanisms remain to be elucidated. Transcriptomic analysis is employed to detect diagnostic biomarkers and reveal shared pathophysiological mechanisms in sepsis-associated TTS. METHODS: Myocardial gene expression data (TTS, sepsis, and controls) from GEO were analyzed to identify shar...

SERS on analyte-enriched blood for rapid, culture-free sepsis recognition and causative pathogen identification with super operational neural networks.

Sepsis remains a leading cause of morbidity and mortality, yet routine diagnostics are slow, culture-dependent, and often lack the sensitivity or spec...

Motion Mitigation Techniques for Abdominal and Cardiac MR Imaging.

MRI of the heart and abdominal organs provides unparalleled soft tissue contrast and quantitative biomarkers, yet remains highly susceptible to physio...

Construction and validation of a machine learning-based risk prediction model for invasive mechanical ventilation in AECOPD patients complicated with respiratory failure.

OBJECTIVE: This study aimed to create and validate a machine learning (ML) model to predict the likelihood of invasive mechanical ventilation (IMV) in...

Integrating multi-omics and artificial intelligence fuels advanced target identification and drug discovery.

Target identification is pivotal for developing novel therapeutics in cancer and other diseases. Traditional experiment screening methods are constrai...

Machine Learning for Intensive Care Unit Length-of-Stay Prediction: A Simulation-Based Approach to Bed Capacity Management.

BackgroundWhile machine learning (ML) models are increasingly used to predict outcomes in health care, their practical effect on health care operation...

AI-assisted qualitative analysis of formative feedback to support student-centered learning in physiology.

This study evaluates the effectiveness of large language models (LLMs), specifically Claude Sonnet 4.0 and ChatGPT 4.1, for analyzing formative feedba...

Automotive Health 2.0: Steering Toward Proactive Preventive Care.

Cardiovascular and chronic disease prevention remains limited by episodic, clinic-based assessments that fail to capture physiological changes arising...

Integrated assessment of total airway count and pneumonia volume on chest computed tomography as a prognostic biomarker for coronavirus disease.

OBJECTIVES: The clinical relevance of computed tomography (CT)-based airway tree structure is unclear. Herein, we used artificial intelligence to segm...

Ensemble machine learning for early mortality risk stratification in septic orthopedic trauma: an international cohort study.

BACKGROUND: Sepsis represents a life-threatening complication in severe orthopedic trauma, significantly increasing short-term mortality risk. Despite...

Causal models and network instability in neurological and psychiatric disorders: Computational and clinical perspectives.

Self-diagnosis-the capacity of a system to detect and correct its own failures-is a defining property of adaptive systems. In the brain, recursive sel...

Widely targeted metabolomics and machine learning identify succinate as a key metabolite in sepsis-associated encephalopathy.

Sepsis-associated encephalopathy (SAE) is a common and serious complication of sepsis that leads to acute brain dysfunction and long-term cognitive im...

The micro- and nanoplastics exposome feedback loop drives synergistic air-lung toxicity.

Airborne micro- and nanoplastics (MNPs) are now recognized as persistent components of the atmospheric exposome. While their presence is established, ...

Predicting postoperative length of stay: a feature selection approach to predictive modeling in lumbar fusion surgery.

BACKGROUND: Predictive modeling has the potential to improve preoperative planning and resource allocation in lumbar fusion surgery. This study aimed ...

Automated lung sound detection via Bi-GRU-modified SqueezeNet architecture with new stock well feature set.

Lung sound analysis is critical for diagnosing respiratory diseases such as asthma, bronchiectasis, bronchiolitis, COPD, LRTI, pneumonia, and URTI. Tr...

TEWS: Transformer-empowered weakly supervised prediction of immune score and genetic mutations in liver cancer from whole slide image.

Whole Slide Imaging (WSI) plays a crucial role in predicting immune scores by providing detailed cellular and tissue-level insights, thereby enhancing...

A multi-branch ConvNeXt-MaxViT fusion transformer model for radiographic knee osteoarthritis severity assessment with Grad-CAM++ explainability.

CONTEXT: Knee Osteoarthritis (KOA) is a progressive degenerative joint disorder and a major cause of disability worldwide. Radiographic grading using ...

MMC-CS: Multi-branch multi-stage contrastive learning for self-supervised compressed sensing.

In recent years, optimization-inspired networks that integrate optimization theory into deep neural networks (DNNs) have achieved remarkable success i...

Biosecurity practices useful for porcine reproductive and respiratory syndrome control and eradication on commercial swine farms using machine learning models.

It is well known that porcine reproductive and respiratory syndrome (PRRS) decreases herd productivity and leads to economic loss, and it is believed ...