AIMC Topic: Aged
Clear Filters Showing 681 to 690 of 12515 articles
Predicting metabolic dysfunction associated steatotic liver disease using explainable machine learning methods.
Scientific reports
Apr 11, 2025
Early and accurate identification of patients at high risk of metabolic dysfunction-associated steatotic liver disease (MASLD) is critical to prevent and improve prognosis potentially. We aimed to develop and validate an explainable prediction model ...
Predicting PD-L1 status in NSCLC patients using deep learning radiomics based on CT images.
Scientific reports
Apr 11, 2025
Radiomics refers to the utilization of automated or semi-automated techniques to extract and analyze numerous quantitative features from medical images, such as computerized tomography (CT) or magnetic resonance imaging (MRI) scans. This study aims t...
Oxidative Stress Markers and Prediction of Severity With a Machine Learning Approach in Hospitalized Patients With COVID-19 and Severe Lung Disease: Observational, Retrospective, Single-Center Feasibility Study.
JMIR formative research
Apr 11, 2025
BACKGROUND: Serious pulmonary pathologies of infectious, viral, or bacterial origin are accompanied by inflammation and an increase in oxidative stress (OS). In these situations, biological measurements of OS are technically difficult to obtain, and ...
Cost-Effectiveness Analysis of a Machine Learning-Based eHealth System to Predict and Reduce Emergency Department Visits and Unscheduled Hospitalizations of Older People Living at Home: Retrospective Study.
JMIR formative research
Apr 11, 2025
BACKGROUND: Dependent older people or those losing their autonomy are at risk of emergency hospitalization. Digital systems that monitor health remotely could be useful in reducing these visits by detecting worsening health conditions earlier. Howeve...
Development of a Machine Learning Algorithm to Predict Abnormalities in Serum Phosphate in a Large Oncology Cohort.
JCO clinical cancer informatics
Apr 11, 2025
PURPOSE: Serum phosphate is commonly measured in oncology patients because of the relationship between oncologic conditions and treatments with abnormal phosphate. All patients attending our institution, a large specialist oncology center, have a sta...
Machine Learning Models of Early Longitudinal Toxicity Trajectories Predict Cetuximab Concentration and Metastatic Colorectal Cancer Survival in the Canadian Cancer Trials Group/AGITG CO.17/20 Trials.
JCO clinical cancer informatics
Apr 11, 2025
PURPOSE: Cetuximab (CET), targeting the epidermal growth factor receptor, is a systemic treatment option for patients with colorectal cancer. One known predictive factor for CET efficacy is the presence of CET-related rash; other putative toxicity fa...
Utilizing machine learning and geographic analysis to improve Post-crash traffic injury management and emergency response systems.
International journal of injury control and safety promotion
Apr 11, 2025
Traffic injuries are a major public health concern globally. This study uses machine learning (ML) and geographic analysis to analyse road traffic fatalities and improve traffic safety in Nakhon Ratchasima Province, Thailand. Data on road traffic fat...
A machine learning approach to risk-stratification of gastric cancer based on tumour-infiltrating immune cell profiles.
Annals of medicine
Apr 10, 2025
BACKGROUND: Gastric cancer (GC) is a highly heterogeneous disease, and the response of patients to clinical treatment varies substantially. There is no satisfactory strategy for predicting curative effects to date. We aimed to explore a new method fo...
Machine-Learning-Based Computed Tomography Radiomics Regression Model for Predicting Pulmonary Function.
Academic radiology
Apr 10, 2025
RATIONALE AND OBJECTIVES: Chest computed tomography (CT) radiomics can be utilized for categorical predictions; however, models predicting pulmonary function indices directly are lacking. This study aimed to develop machine-learning-based regression ...
Interpretable Machine Learning Radiomics Model Predicts 5-year Recurrence-Free Survival in Non-metastatic Clear Cell Renal Cell Carcinoma: A Multicenter and Retrospective Cohort Study.
Academic radiology
Apr 10, 2025
RATIONALE AND OBJECTIVES: To develop and validate a computed tomography (CT) radiomics-based interpretable machine learning (ML) model for predicting 5-year recurrence-free survival (RFS) in non-metastatic clear cell renal cell carcinoma (ccRCC).
Don't Miss the Future of Medicine
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 31261
- Neural Networks, Computer 29817
- Algorithms 26965
- Deep Learning 26491
- Artificial Intelligence 24413
- Robotics 13329
- Image Processing, Computer-Assisted 9578
- Retrospective Studies 9155
- Magnetic Resonance Imaging 6213
- Reproducibility of Results 5605
- Tomography, X-Ray Computed 4792
- Support Vector Machine 4705
- Computational Biology 4266
- Brain 4057
- Computer Simulation 3891
Recent Journals
- Scientific reports 6194
- Sensors (Basel, Switzerland) 4794
- PloS one 4069
- Neural networks : the official journal of the International Neural Network Society 2930
- Computational intelligence and neuroscience 1931
- Computers in biology and medicine 1868
- 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 1143
- Journal of chemical information and modeling 1017
- Nature communications 970
- Computer methods and programs in biomedicine 883
- Bioinformatics (Oxford, England) 863
- Briefings in bioinformatics 853
- IEEE transactions on neural networks and learning systems 831