AIMC Topic: Machine Learning
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Sleep disturbances and PTSD: identifying baseline predictors of insomnia response in an intensive treatment programme.
European journal of psychotraumatology
Jun 23, 2025
This study examined whether baseline demographic and clinical variables could predict clinically significant reductions in insomnia symptoms among veterans receiving a 2-week Cognitive Processing Therapy (CPT)-based intensive PTSD treatment programm...
CellMemory: hierarchical interpretation of out-of-distribution cells using bottlenecked transformer.
Genome biology
Jun 23, 2025
Machine learning methods, especially Transformer architectures, have been widely employed in single-cell omics studies. However, interpretability and accurate representation of out-of-distribution (OOD) cells remains challenging. Inspired by the glob...
Harnessing the machine learning and nomogram models: elevating prognostication in nonmetastatic gastric cancer with "double invasion" for personalized patient care.
European journal of medical research
Jun 23, 2025
OBJECTIVE: To develop and validate a machine learning framework combined with a nomogram for predicting recurrence after radical gastrectomy in patients with vascular and neural invasion.
Classification of primary glomerulonephritis using machine learning models: a focus on IgA nephropathy prediction.
BMC nephrology
Jun 23, 2025
OBJECTIVE: IgA nephropathy (IgAN) is the most common form of glomerulonephritis worldwide, characterized by immune complex deposition in the glomerular mesangium, leading to mesangial hypercellularity, persistent microhematuria, proteinuria, and prog...
Inflammation-related 5-hydroxymethylation signatures as markers for clinical presentations of coronary artery disease.
Cardiovascular diabetology
Jun 23, 2025
BACKGROUND: Coronary angiography remains the gold standard for diagnosing coronary artery disease (CAD), but its invasive nature limits its applicability for widespread screening. Identifying non-invasive molecular markers could improve CAD classific...
ST Elevation Myocardial Infarction
Coronary Artery Disease
Inflammation Mediators
Cell-Free Nucleic Acids
Non-ST Elevated Myocardial Infarction
Predictive Value of Tests
5-Methylcytosine
Middle Aged
Risk Assessment
Severity of Illness Index
Epigenesis, Genetic
Inflammation
DNA Methylation
Humans
Biomarkers
Machine Learning
Reproducibility of Results
Prognosis
Aged
Female
Male
Exploring Factors Related to Social Isolation Among Older Adults in the Predementia Stage Using Ecological Momentary Assessments and Actigraphy: Machine Learning Approach.
Journal of medical Internet research
Jun 23, 2025
BACKGROUND: As the global population ages, the economic burden of dementia continues to rise. Social isolation-which includes limited social interaction and feelings of loneliness-negatively affects cognitive function and is a significant risk factor...
MSFCL: Drug Combination Risk Level Prediction Based on Multi-Source Feature Fusion and Contrastive Learning.
Journal of chemical information and modeling
Jun 20, 2025
Accurate assessment of drug combination risk levels is crucial for guiding rational clinical medication and avoiding adverse reactions. However, most existing methods are limited to binary classification, which fails to quantify distinctions between ...
CSU-MS: A Contrastive Learning Framework for Cross-Modal Compound Identification from MS/MS Spectra to Molecular Structures.
Analytical chemistry
Jun 20, 2025
Tandem mass spectrometry (MS/MS) is a cornerstone for compound identification in complex mixtures, but conventional spectral matching approaches face critical limitations due to limited library coverage and matching algorithms. To address this, we pr...
A machine learning model for mortality prediction in patients with severe fever with thrombocytopenia syndrome: a prospective, multicenter cohort study.
Emerging microbes & infections
Jun 19, 2025
Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease that imposes a considerable medical burden. In this study, we enrolled 1,606 SFTS patients, developed and validated machine learning models for mortality prediction,...
Development of a Machine Learning-Based Predictive Model for Postoperative Delirium in Older Adult Intensive Care Unit Patients: Retrospective Study.
Journal of medical Internet research
Jun 19, 2025
BACKGROUND: Delirium is a prevalent phenomenon among patients admitted to the geriatric intensive care unit (ICU) and can adversely impact prognosis and augment the risk of complications.
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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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