AIMC Topic: Machine Learning
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Identifying patterns of high intraoperative blood pressure variability in noncardiac surgery using explainable machine learning: a retrospective cohort study.
Annals of medicine
Jul 24, 2025
BACKGROUND: High intraoperative blood pressure variability (HIBPV) is significantly associated with postoperative adverse complications. However, practical tools to characterize perioperative factors associated with HIBPV remain limited. This study a...
Biomacromolecular engineering of redox-active chitosan-polypyrrole-clay hybrid materials for machine learning-assisted desulfurization and supercapacitor application.
International journal of biological macromolecules
Jul 24, 2025
The bentonite chitosan polypyrrole (Bent-CS-PPy) composite was engineered as a multifunctional material with dual capabilities: the efficient adsorption of dibenzothiophene (DBT) from model fuel and application as an electrode in electrochemical ener...
Integrating Non-Targeted Mass Spectrometry and Machine Learning for the Classification of Organic and Conventionally Grown Agricultural Products: A Case Study on Tomatoes.
Journal of agricultural and food chemistry
Jul 24, 2025
Rising demand for organic agricultural products has made the verification of their authenticity a critical concern. Traditional classification approaches for mass spectrometry using full-scan high-resolution mass spectrometry data often emphasize fea...
Machine Learning-Driven Dynamic Measurement of Environmental Indicators in Multiple Scenes and Multiple Disturbances.
Environmental science & technology
Jul 24, 2025
Digital city water management systems require extensive data sensing for various environmental indicators, yet measurement accuracy often falls short under diverse extreme conditions. This study proposes a chemical oxygen demand (COD) measurement met...
Machine learning identification of key genes in cardioembolic stroke and atherosclerosis: their association with pan-cancer and immune cells.
European journal of medical research
Jul 24, 2025
BACKGROUND: Cardioembolic stroke (CS) and atherosclerosis (AS) are closely related diseases. Ferroptosis, a novel form of programmed cell death, may play a key role in CS and AS. However, the pathophysiological mechanisms underlying their coexistence...
How to use learning curves to evaluate the sample size for malaria prediction models developed using machine learning algorithms.
Malaria journal
Jul 24, 2025
BACKGROUND: Machine learning algorithms have been used to predict malaria risk and severity, identify immunity biomarkers for malaria vaccine candidates, and determine molecular biomarkers of antimalarial drug resistance. Developing these prediction ...
Development and validation of machine learning-based risk prediction models for ICU-acquired weakness: a prospective cohort study.
European journal of medical research
Jul 24, 2025
BACKGROUND: Intensive care unit (ICU)-acquired weakness (ICUAW) is a prevalent complication in critically ill patients, marked by symmetrical respiratory and limb muscle weakness, which adversely affects long-term outcomes. Early identification of hi...
Optimized feature selection and advanced machine learning for stroke risk prediction in revascularized coronary artery disease patients.
BMC medical informatics and decision making
Jul 24, 2025
BACKGROUND: Coronary artery disease (CAD) remains a leading cause of global mortality, with stroke constituting a significant complication among patients undergoing coronary revascularization procedures, such as percutaneous coronary intervention (PC...
Interpretable machine learning-guided single-cell mapping deciphers multi-lineage pancreatic dysregulation in type 2 diabetes.
Cardiovascular diabetology
Jul 24, 2025
BACKGROUND: Pancreatic cellular heterogeneity is fundamental to systemic metabolic regulation, yet its pathological remodeling in diabetes remains poorly characterized.
Precision nutrition in epigenetic aging: SHAP-optimized machine learning identifies omega-3 constituent-specific associations with aging biomarkers.
Biogerontology
Jul 24, 2025
This cross-sectional investigation seeks to examine the association between dietary omega-3 fatty acids (including α-linolenic acid [ALA], eicosapentaenoic acid [EPA], and docosahexaenoic acid [DHA]) and biomarkers of cellular aging, specifically DNA...
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- Machine Learning 32531
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- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
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