AIMC Topic: Age Factors
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Self-training EEG discrimination model with weakly supervised sample construction: An age-based perspective on ASD evaluation.
Neural networks : the official journal of the International Neural Network Society
Jul 1, 2025
Deep learning for Electroencephalography (EEG) has become dominant in the tasks of discrimination and evaluation of brain disorders. However, despite its significant successes, this approach has long been facing challenges due to the limited availabi...
EVALUATION OF PROGNOSTIC RISK MODELS BASED ON AGE AND COMORBIDITY IN SEPTIC PATIENTS: INSIGHTS FROM MACHINE LEARNING AND TRADITIONAL METHODS IN A LARGE-SCALE, MULTICENTER, RETROSPECTIVE STUDY.
Shock (Augusta, Ga.)
Jul 1, 2025
Background: Age and comorbidity significantly impact the prognosis of septic patients and inform treatment decisions. To provide clinicians with effective tools for identifying high-risk patients, this study assesses the predictive value of the age-a...
Hybrid time series and machine learning models for forecasting cardiovascular mortality in India: an age specific analysis.
BMC public health
Jun 10, 2025
Cardiovascular disease (CVD) is a primary cause of death in India, accounting for a significant portion of the global CVD burden. This study looks at statistics on heart disease mortality from the Institute for Health Metrics and Evaluation (IHME) fr...
IdenBAT: Disentangled representation learning for identity-preserved brain age transformation.
Artificial intelligence in medicine
Jun 1, 2025
Brain age transformation aims to convert reference brain images into synthesized images that accurately reflect the age-specific features of a target age group. The primary objective of this task is to modify only the age-related attributes of the re...
Potentially inappropriate polypharmacy is an important predictor of 30-day emergency hospitalisation in older adults: a machine learning feature validation study.
Age and ageing
May 31, 2025
BACKGROUND: Machine learning (ML) models in healthcare are crucial for predicting clinical outcomes, and their effectiveness can be significantly enhanced through improvements in accuracy, generalisability, and interpretability. To achieve widespread...
Inappropriate Prescribing
Potentially Inappropriate Medication List
Age Factors
Emergency Service, Hospital
Geriatric Assessment
United Kingdom
Polypharmacy
Aged, 80 and over
Male
Risk Assessment
Time Factors
Humans
Machine Learning
Female
Reproducibility of Results
Risk Factors
Hospitalization
Aged
Stroke prediction in elderly patients with atrial fibrillation using machine learning combined clinical and left atrial appendage imaging phenotypic features.
BMC cardiovascular disorders
May 24, 2025
BACKGROUND: Atrial fibrillation (AF) is one of the primary etiologies for ischemic stroke, and it is of paramount importance to delineate the risk phenotypes among elderly AF patients and to investigate more efficacious models for predicting stroke r...
Incidence
Support Vector Machine
Age Factors
Predictive Value of Tests
Ischemic Stroke
Aged
Aged, 80 and over
Male
Risk Assessment
Stroke
Atrial Fibrillation
Computed Tomography Angiography
Atrial Appendage
Humans
Machine Learning
Decision Support Techniques
Phenotype
Prospective Studies
Prognosis
Female
Risk Factors
Assessing artificial intelligence in breast screening with stratified results on 306 839 mammograms across geographic regions, age, breast density and ethnicity: A Retrospective Investigation Evaluating Screening (ARIES) study.
BMJ health & care informatics
May 14, 2025
OBJECTIVES: Evaluate an Artificial Intelligence (AI) system in breast screening through stratified results across age, breast density, ethnicity and screening centres, from different UK regions.
Machine learning-based Diagnostic model for determining the etiology of pleural effusion using Age, ADA and LDH.
Respiratory research
May 2, 2025
BACKGROUND: Classification of the etiologies of pleural effusion is a critical challenge in clinical practice. Traditional diagnostic methods rely on a simple cut-off method based on the laboratory tests. However, machine learning (ML) offers a novel...
Identifying individuals at risk of post-stroke depression: Development and validation of a predictive model.
Saudi medical journal
May 1, 2025
OBJECTIVES: To identify the factors associated with post-stroke depression (PSD) and develop a machine learning predictive model using a large dataset, considering sociodemographic, lifestyle, and clinical factors.
Determinants of ascending aortic morphology: cross-sectional deep learning-based analysis on 25 073 non-contrast-enhanced NAKO MRI studies.
European heart journal. Cardiovascular Imaging
Apr 30, 2025
AIMS: Understanding determinants of thoracic aortic morphology is crucial for precise diagnostics and therapeutic approaches. This study aimed to automatically characterize ascending aortic morphology based on 3D non-contrast-enhanced magnetic resona...
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