AIMC Topic: Machine Learning

Clear Filters Showing 22321 to 22330 of 34417 articles

Machine learning as new promising technique for selection of significant features in obese women with type 2 diabetes.

Hormone molecular biology and clinical investigation
Background The global trend of obesity and diabetes is considerable. Recently, the early diagnosis and accurate prediction of type 2 diabetes mellitus (T2DM) patients have been planned to be estimated according to precise and reliable methods, artifi...

Application of artificial neural networks for automated analysis of cystoscopic images: a review of the current status and future prospects.

World journal of urology
BACKGROUND: Optimal detection and surveillance of bladder cancer (BCa) rely primarily on the cystoscopic visualization of bladder lesions. AI-assisted cystoscopy may improve image recognition and accelerate data acquisition.

Cost-effectiveness of targeted screening for the identification of patients with atrial fibrillation: evaluation of a machine learning risk prediction algorithm.

Journal of medical economics
As many cases of atrial fibrillation (AF) are asymptomatic, patients often remain undiagnosed until complications (e.g. stroke) manifest. Risk-prediction algorithms may help to efficiently identify people with undiagnosed AF. However, the cost-effec...

Polygenic risk scores outperform machine learning methods in predicting coronary artery disease status.

Genetic epidemiology
Coronary artery disease (CAD) is the leading global cause of mortality and has substantial heritability with a polygenic architecture. Recent approaches of risk prediction were based on polygenic risk scores (PRS) not taking possible nonlinear effect...

Label-Free Tomographic Imaging of Lipid Droplets in Foam Cells for Machine-Learning-Assisted Therapeutic Evaluation of Targeted Nanodrugs.

ACS nano
Lipid droplet (LD) accumulation, a key feature of foam cells, constitutes an attractive target for therapeutic intervention in atherosclerosis. However, despite advances in cellular imaging techniques, current noninvasive and quantitative methods hav...

Systematic Modeling of log  Based on Ensemble Machine Learning, Group Contribution, and Matched Molecular Pair Analysis.

Journal of chemical information and modeling
Lipophilicity, as evaluated by the -octanol/buffer solution distribution coefficient at pH = 7.4 (log ), is a major determinant of various absorption, distribution, metabolism, elimination, and toxicology (ADMET) parameters of drug candidates. In thi...

Prediction of Myopia in Adolescents through Machine Learning Methods.

International journal of environmental research and public health
According to literature, myopia has become the second most common eye disease in China, and the incidence of myopia is increasing year by year, and showing a trend of younger age. Previous researches have shown that the occurrence of myopia is mainly...

Machine-learning facilitates selection of a novel diagnostic panel of metabolites for the detection of heart failure.

Scientific reports
The metabolic derangement is common in heart failure with reduced ejection fraction (HFrEF). The aim of the study was to check feasibility of the combined approach of untargeted metabolomics and machine learning to create a simple and potentially cli...

Machine Learning Supports Long Noncoding RNAs as Expression Markers for Endometrial Carcinoma.

BioMed research international
Uterine corpus endometrial carcinoma (UCEC) is the second most common type of gynecological tumor. Several research studies have recently shown the potential of different ncRNAs as biomarkers for prognostics and diagnosis in different types of cancer...