AIMC Topic: Age Factors
Clear Filters Showing 1 to 10 of 358 articles
Predicting Early-Onset Colorectal Cancer in Individuals Below Screening Age Using Machine Learning and Real-World Data: Case Control Study.
JMIR cancer
Jun 19, 2025
BACKGROUND: Colorectal cancer is now the leading cause of cancer-related deaths among young Americans. Accurate early prediction and a thorough understanding of the risk factors for early-onset colorectal cancer (EOCRC) are vital for effective preven...
Uncovering nonlinear patterns in time-sensitive prehospital breathing emergencies: an exploratory machine learning study.
BMC medical informatics and decision making
Jun 3, 2025
BACKGROUND: Timely prehospital care is crucial for patients presenting with high-risk time-sensitive (HRTS) conditions. However, the interplay between response time and demographic factors in patients with breathing problems remains insufficiently un...
Developing cardiac digital twin populations powered by machine learning provides electrophysiological insights in conduction and repolarization.
Nature cardiovascular research
May 16, 2025
Large-cohort imaging and diagnostic studies often assess cardiac function but overlook underlying biological mechanisms. Cardiac digital twins (CDTs) are personalized physics-constrained and physiology-constrained in silico representations, uncoverin...
Identification of Risk Group for Root Caries and Analysis of Associated Factors in Older Adults Using Unsupervised Machine Learning Clustering.
Clinical interventions in aging
Apr 24, 2025
PURPOSE: This study aimed to identify the high-risk group for root caries using unsupervised machine learning and to explore the associated factors.
The Association of Elevated Depression Levels and Life's Essential 8 on Cardiovascular Health With Predicted Machine Learning Models and Interpretations: Evidence From NHANES 2007-2018.
Depression and anxiety
Apr 10, 2025
The association between depression severity and cardiovascular health (CVH) represented by Life's Essential 8 (LE8) was analyzed, with a novel focus on ranked levels and different ages. Machine learning (ML) algorithms were also selected aimed at pr...
Biological age prediction and NAFLD risk assessment: a machine learning model based on a multicenter population in Nanchang, Jiangxi, China.
BMC gastroenterology
Mar 13, 2025
BACKGROUND: The objective was to develop a biological age prediction model (NC-BA) for the Chinese population to enrich the relevant studies in this population. And to investigate the association between accelerated age and NAFLD.
Cold storage surpasses the impact of biological age and donor characteristics on red blood cell morphology classified by deep machine learning.
Scientific reports
Mar 5, 2025
Assessment of the morphology of red blood cells (RBCs) can improve clinical benefits following blood transfusion. Deep machine learning surpasses traditional microscopy-based classification methods, offering more accurate and consistent results while...
Predictive modeling with linear machine learning can estimate glioblastoma survival in months based solely on MGMT-methylation status, age and sex.
Acta neurochirurgica
Feb 24, 2025
PURPOSE: Machine Learning (ML) has become an essential tool for analyzing biomedical data, facilitating the prediction of treatment outcomes and patient survival. However, the effectiveness of ML models heavily relies on both the choice of algorithms...
Associations between age, red cell distribution width and 180-day and 1-year mortality in giant cell arteritis patients: mediation analyses and machine learning in a cohort study.
Arthritis research & therapy
Feb 8, 2025
OBJECTIVE: The aim of this study was to investigate the correlation between age, red cell distribution width (RDW) levels, and 180-day and 1-year mortality in giant cell arteritis (GCA) patients hospitalized or admitted to the ICU.
Analyzing patterns of frequent mental distress in Alzheimer's patients: A generative AI approach.
Journal of the National Medical Association
Feb 8, 2025
This study tackles creating Python code for beginners with generative AI and analyzing trends in mental distress among Alzheimer's patients in the US (2015-2022 CDC data). It guides beginners through using AI to generate code for visualizing these tr...
Stay Ahead of Medical AI
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.
Subscribe NowPopular Topics
- Machine Learning 30978
- Neural Networks, Computer 29696
- Algorithms 26813
- Deep Learning 26324
- Artificial Intelligence 24267
- Robotics 13307
- Image Processing, Computer-Assisted 9512
- Retrospective Studies 9097
- Magnetic Resonance Imaging 6173
- Reproducibility of Results 5589
- Tomography, X-Ray Computed 4765
- Support Vector Machine 4678
- Computational Biology 4229
- Brain 4045
- Computer Simulation 3877
Recent Journals
- Scientific reports 5865
- Sensors (Basel, Switzerland) 4794
- PloS one 4011
- Neural networks : the official journal of the International Neural Network Society 2928
- Computational intelligence and neuroscience 1931
- Computers in biology and medicine 1839
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
- Studies in health technology and informatics 1278
- IEEE journal of biomedical and health informatics 1129
- Journal of chemical information and modeling 994
- Nature communications 938
- Computer methods and programs in biomedicine 883
- Bioinformatics (Oxford, England) 861
- Briefings in bioinformatics 850
- IEEE transactions on neural networks and learning systems 823