Intensivists

Machine Learning Models for Mortality Prediction in Intensive Care Unit Patients With Ischemic Stroke Associated With Intracranial Artery Stenosis: Retrospective Cohort Study.

BACKGROUND: Mortality prediction in intensive care unit (ICU) patients with ischemic stroke complicated by intracranial artery stenosis or occlusion remains difficult. Conventional scoring systems often lack discriminatory power and fail to provide individualized risk estimates. Machine learning approaches have been increasingly explored to integrate diverse clinical features for prognostic modeli...

Machine learning guided resolution of mechanical trade-off in polymer composites via stress adaptive interface.

Developing polymer composites that simultaneously achieve high strength, toughness, and impact resistance remains a fundamental challenge due to inherent trade-offs and brittle interfacial failure. Here, we propose a universal toughening strategy that integrates a bone-inspired trabecular interlock architecture with a thermodynamically driven, stress-adaptive interface to enable efficient energy d...

Natriuretic peptides and cardiac troponins in hypertension and heart failure: A contemporary review of the evidence.

Hypertension is the second leading cause of heart failure (HF), yet strategies for identifying hypertensive individuals at increased HF risk remain li...

Integration of single-cell and bulk transcriptomics with machine learning identifies LDHA as a lactate-related diagnostic and prognostic biomarker in sepsis.

A fuzzy ZE-number group decision-making framework using BWM and MABAC for risk assessment in medicinal plant extraction.

The extraction of bioactive compounds from medicinal plants is a crucial process in pharmaceutical and herbal medicine industries, yet it presents num...

Lean mass loss in the intensive care unit and its restoration: A narrative review.

The preservation of lean mass (LM) and its restoration following catabolic loss represents a primary challenge for clinical nutrition in critically il...

Advancing Coronary Risk Assessment Through Combined Radiomic, Mechanical, and Hemodynamic Analysis.

PURPOSE: Detecting vulnerable coronary plaques through coronary computed tomography angiography (CCTA) is a crucial, yet challenging task. To date, mo...

Acute deep neck infection MRI: deep learning segmentation and clinical relevance of retropharyngeal edema volume.

OBJECTIVE: Retropharyngeal edema (RPE) on MRI in patients with acute neck infection is associated with disease severity. We explored the potential rol...

From graph theory to chemoinformatics: modified bond-based indices and a hypothesis-driven multi-task QSAR/QSPR benchmark.

Graph-theoretic degree-based descriptors play a central role in chemoinformatics and QSPR/QSAR modelling, yet most classical indices either focus pure...

Development and validation of a machine learning-based nomogram model integrating LASSO regression and multiple evaluation methods for predicting early sepsis risk in acute abdomen.

BACKGROUND: Sepsis secondary to acute abdomen is a critical driver of clinical deterioration and mortality, with outcomes closely linked to sepsis sev...

Prediction of COVID-19 hospitalisation, ICU admission or death following ChAdOx1 vaccination using artificial intelligence: A clinical predictive model from the English RAVEN study.

OBJECTIVES: This study identifies predictors of severe COVID-19 following completion of two-dose primary series of the AZD1222 COVID-19 vaccine, emplo...

SCMO: a deep learning model integrating the single-cell resolution TME ecosystem and multi-omics for survival prediction in CRC patients.

BACKGROUND: Colorectal cancer (CRC) remains a leading cause of global cancer mortality, highlighting the need for precise survival prediction to guide...

Insights on Recruitment, Implementation, and Movement Pattern Detection by Exploring the Feasibility of Sensor-Based Insole Technology in Long-Term Care: Mixed Methods Feasibility Study.

BACKGROUND: Sensor-based footwear is increasingly discussed as a promising tool for mobility monitoring and fall-risk assessment, yet its applicabilit...

Interpretable machine learning-based real-time sepsis diagnosis.

Physically reconfigurable synaptic plasticity and learning in stretchable neuromorphic systems.

Wearable electronics that use intrinsically stretchable organic neuromorphic devices offer a promising approach to achieve human-like on-device proces...

Multi-encoder U-Net benchmarking for LiTS17 Liver-Tumor segmentation: accuracy-efficiency trade-offs across training durations.

OBJECTIVES: Accurate liver and tumor segmentation from CT is fundamental for diagnosis, treatment planning, and longitudinal monitoring of liver cance...

Sepsis Diagnostics via Biosensors: Engineering Platforms, Artificial Intelligence Integration, and Clinical Translation.

Sepsis remains one of the most diagnostically challenging syndromes due to its clinical heterogeneity, overlapping host-pathogen responses, and lack o...

Emerging Advanced Electronic Packaging Materials for Thermal Management in Power Electronics.

Current research on integrated circuits and power electronics is rapidly advancing toward miniaturization, high power density, and multi-chip integrat...

Artificial intelligence-based prediction of postoperative sepsis in patients undergoing suction mini-percutaneous nephrolithotomy in real world practice: a multicenter analysis from the global Suction Lechnology Utility in Mini-PCNL Study (STUMPS) registry.

Personalized sepsis mortality prediction: An interpretable machine learning nomogram.

PURPOSE: Sepsis remains a major cause of mortality in ICU patients, requiring accurate prognostic tools for optimal management. This study aimed to de...