Primary Care

Latest AI and machine learning research in primary care for healthcare professionals.

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Cross-sectional design and protocol for Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI).

INTRODUCTION: Artificial Intelligence Ready and Equitable for Diabetes Insights (AI-READI) is a data...

Transfer Learning Prediction of Early Exposures and Genetic Risk Score on Adult Obesity in Two Minority Cohorts.

Due to ethnic heterogeneity in genetic architecture, genetic risk score (GRS) constructed within the...

GARNN: An interpretable graph attentive recurrent neural network for predicting blood glucose levels via multivariate time series.

Accurate prediction of future blood glucose (BG) levels can effectively improve BG management for pe...

REMED-T2D: A robust ensemble learning model for early detection of type 2 diabetes using healthcare dataset.

Early diagnosis and timely treatment of diabetes are critical for effective disease management and t...

Estimation of Hematocrit Volume Using Blood Glucose Concentration through Extreme Gradient Boosting Regressor Machine Learning Model.

Lifestyle diseases such as cardiovascular disorders, diabetes, etc. affect the physiological metabol...

Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements.

Chronic kidney disease (CKD) is a global health concern with early detection playing a pivotal role ...

Identification of Clusters in a Population With Obesity Using Machine Learning: Secondary Analysis of The Maastricht Study.

BACKGROUND: Modern lifestyle risk factors, like physical inactivity and poor nutrition, contribute t...

Advances in the application of human-machine collaboration in healthcare: insights from China.

In the context of the technological revolution and the digital intelligence era, the contradiction b...

Machine learning prediction of obesity-associated gut microbiota: identifying as a potential therapeutic target.

BACKGROUND: The rising prevalence of obesity and related metabolic disorders highlights the urgent n...

Deep learning opportunistic screening for osteoporosis and osteopenia using radiographs of the foot or ankle - A pilot study.

BACKGROUND: The gold standard method for diagnosing low bone mineral density (BMD) is using dual-ene...

Modeling the latent impacts of extreme floods on indoor mold spores in residential buildings: Application of machine learning algorithms.

Floods can severely impact the economy, environment and society. These impacts can be direct and ind...

Rim learning framework based on TS-GAN: A new paradigm of automated glaucoma screening from fundus images.

Glaucoma detection from fundus images often relies on biomarkers such as the Cup-to-Disc Ratio (CDR)...

Convolutional Neural Networks Assisted Peak Classification in Targeted LC-HRMS/MS for Equine Doping Control Screening Analyses.

Doping control screening analyses usually involve visual inspection of extracted ion chromatograms (...

Development and Evaluation of a Deep Learning-Based Pulmonary Hypertension Screening Algorithm Using a Digital Stethoscope.

BACKGROUND: Despite the poor outcomes related to the presence of pulmonary hypertension, it often go...

Prediction of the Risk of Adverse Clinical Outcomes with Machine Learning Techniques in Patients with Noncommunicable Diseases.

Decision-making in chronic diseases guided by clinical decision support systems that use models incl...

[Use of Artificial Intelligence in Diabetic Retinopathy Screening: Experience in a Health Service in Santiago, Chile].

UNLABELLED: Early detection of diabetic retinopathy is critical for preventing vision loss.

PPARγ modulator predictor (PGMP_v1): chemical space exploration and computational insights for enhanced type 2 diabetes mellitus management.

Peroxisome proliferator-activated receptor gamma (PPARγ) plays a critical role in adipocyte differen...

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