AIMC Topic: Age Factors
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Ensemble approach for predicting the diagnosis of osteoarthritis using physical activity factors.
Journal of evaluation in clinical practice
Oct 23, 2024
BACKGROUND: Osteoarthritis (OA) is a common degenerative disease of the joints. Risk factors for OA include non-modifiable factors such as age and sex, as well as modifiable factors like physical activity.
Patient-Specific Myocardial Infarction Risk Thresholds From AI-Enabled Coronary Plaque Analysis.
Circulation. Cardiovascular imaging
Sep 30, 2024
BACKGROUND: Plaque quantification from coronary computed tomography angiography has emerged as a valuable predictor of cardiovascular risk. Deep learning can provide automated quantification of coronary plaque from computed tomography angiography. We...
Artificial intelligence age prediction using electrocardiogram data: Exploring biological age differences.
Heart rhythm
Sep 27, 2024
BACKGROUND: Biological age can be predicted using artificial intelligence (AI) trained on electrocardiograms (ECGs), which is prognostic for mortality and cardiovascular events.
A case for the use of deep learning algorithms for individual and population level assessments of mental health disorders: Predicting depression among China's elderly.
Journal of affective disorders
Sep 24, 2024
BACKGROUND: With the continuous advancement of age in China, attention should be paid to the mental well-being of the elderly population. The present study uses a novel machine learning (ML) method on a large representative elderly database in China ...
Identifying biological markers and sociodemographic factors that influence the gap between phenotypic and chronological ages.
Informatics for health & social care
Sep 24, 2024
INTRODUCTION: The world's population is aging rapidly, leading to increased public health and economic burdens due to age-related cardiovascular and neurodegenerative diseases. Early risk detection is essential for prevention and to improve the quali...
Enhancing pharmacist intervention targeting based on patient clustering with unsupervised machine learning.
Expert review of pharmacoeconomics & outcomes research
Sep 23, 2024
OBJECTIVES: Adherence to the American Diabetes Association (ADA) Standards of Medical Care is low. This study aimed to assist pharmacists in identifying patients for diabetes control interventions using unsupervised machine learning.
Professional Role
Pharmaceutical Services
Insurance, Health
Self Report
Sex Factors
Aged
Diabetes Complications
Adult
Pharmacists
Male
Humans
Middle Aged
Age Factors
United States
Machine Learning
Surveys and Questionnaires
Hypoglycemic Agents
Cluster Analysis
Female
Diabetes Mellitus
Unsupervised Machine Learning
What explains adolescents' physical activity and sports participation during the COVID-19 pandemic? - an interpretable machine learning approach.
Journal of sports sciences
Sep 19, 2024
Adolescents' physical activity (PA) and sports participation declined due to the COVID-19 pandemic. This study aimed to determine the critical socio-ecological factors for PA and sports participation using a machine learning approach. We did a cross-...
Image-based ECG analyzing deep-learning algorithm to predict biological age and mortality risks: interethnic validation.
Journal of cardiovascular medicine (Hagerstown, Md.)
Sep 12, 2024
BACKGROUND: Cardiovascular risk assessment is a critical component of healthcare, guiding preventive and therapeutic strategies. In this study, we developed and evaluated an image-based electrocardiogram (ECG) analyzing an artificial intelligence (AI...
Using machine learning methods to investigate the impact of age on the causes of death in patients with early intrahepatic cholangiocarcinoma who underwent surgery.
Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
Sep 11, 2024
BACKGROUND: The impact of age on the causes of death (CODs) in patients with early-stage intrahepatic cholangiocarcinoma (ICC) who had undergone surgery was analyzed in this study.
Rule-based natural language processing to examine variation in worsening heart failure hospitalizations by age, sex, race and ethnicity, and left ventricular ejection fraction.
American heart journal
Sep 7, 2024
BACKGROUND: Prior studies characterizing worsening heart failure events (WHFE) have been limited in using structured healthcare data from hospitalizations, and with little exploration of sociodemographic variation. The current study examined the impa...
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