Primary Care

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

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Predictive modeling for identification of older adults with high utilization of health and social services.

AIM: Machine learning techniques have demonstrated success in predictive modeling across various cli...

Docking Score ML: Target-Specific Machine Learning Models Improving Docking-Based Virtual Screening in 155 Targets.

In drug discovery, molecular docking methods face challenges in accurately predicting energy. Scorin...

Prediction of chromosomal abnormalities in the screening of the first trimester of pregnancy using machine learning methods: a study protocol.

BACKGROUND: For women in the first trimester, amniocentesis or chorionic villus sampling is recommen...

Does clinical practice supported by artificial intelligence improve hypertension care management? A pilot systematic review.

Although artificial intelligence (AI) is considered to be a promising tool, evidence for the effecti...

Identification of key biomarkers for early warning of diabetic retinopathy using BP neural network algorithm and hierarchical clustering analysis.

Diabetic retinopathy is one of the most common microangiopathy in diabetes, essentially caused by ab...

A Coarse-Fine Collaborative Learning Model for Three Vessel Segmentation in Fetal Cardiac Ultrasound Images.

Congenital heart disease (CHD) is the most frequent birth defect and a leading cause of infant morta...

A deep neural network prediction method for diabetes based on Kendall's correlation coefficient and attention mechanism.

Diabetes is a chronic disease, which is characterized by abnormally high blood sugar levels. It may ...

Application of Proteomics and Machine Learning Methods to Study the Pathogenesis of Diabetic Nephropathy and Screen Urinary Biomarkers.

Diabetic nephropathy (DN) has become the main cause of end-stage renal disease worldwide, causing si...

Revealing Comprehensive Food Functionalities and Mechanisms of Action through Machine Learning.

Foods possess a range of unexplored functionalities; however, fully identifying these functions thro...

Scaffold-Hopped Compound Identification by Ligand-Based Approaches with a Prospective Affinity Test.

Scaffold-hopped (SH) compounds are bioactive compounds structurally different from known active comp...

A machine learning tool for identifying patients with newly diagnosed diabetes in primary care.

BACKGROUND AND AIM: It is crucial to identify a diabetes diagnosis early. Create a predictive model ...

Artificial Intelligence Methods for the Argenta Classification of Deformational Plagiocephaly to Predict Severity and Treatment Recommendation.

INTRODUCTION: Deformational plagiocephaly (DP) can be classified into 5 severity types using the Arg...

Non-invasive screening of bladder cancer using digital microfluidics and FLIM technology combined with deep learning.

Non-invasive screening for bladder cancer is crucial for treatment and postoperative follow-up. This...

Advances in AI-assisted biochip technology for biomedicine.

The integration of biochips with AI opened up new possibilities and is expected to revolutionize sma...

Plasma infrared fingerprinting with machine learning enables single-measurement multi-phenotype health screening.

Infrared spectroscopy is a powerful technique for probing the molecular profiles of complex biofluid...

Machine learning algorithms for predicting COVID-19 mortality in Ethiopia.

BACKGROUND: Coronavirus disease 2019 (COVID-19), a global public health crisis, continues to pose ch...

Prediction model for cardiovascular disease in patients with diabetes using machine learning derived and validated in two independent Korean cohorts.

This study aimed to develop and validate a machine learning (ML) model tailored to the Korean popula...

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