Primary Care

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

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Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method.

BACKGROUND: Current lung cancer screening guidelines use mean diameter, volume or density of the lar...

Psychosocial Factors Affecting Artificial Intelligence Adoption in Health Care in China: Cross-Sectional Study.

BACKGROUND: Poor quality primary health care is a major issue in China, particularly in blindness pr...

Novel Screening Method Identifies PI3Kα, mTOR, and IGF1R as Key Kinases Regulating Cardiomyocyte Survival.

Background Small molecule kinase inhibitors (KIs) are a class of agents currently used for treatment...

Utility of Big Data in Predicting Short-Term Blood Glucose Levels in Type 1 Diabetes Mellitus Through Machine Learning Techniques.

Machine learning techniques combined with wearable electronics can deliver accurate short-term blood...

Predictive models for diabetes mellitus using machine learning techniques.

BACKGROUND: Diabetes Mellitus is an increasingly prevalent chronic disease characterized by the body...

Screening physicochemical, microbiological and bioactive properties of fruit vinegars produced from various raw materials.

In the present study, variety of fruit vinegars were investigated in terms of their physicochemical,...

Sepsis in Latent Autoimmune Diabetes in Adults with Diabetic Ketoacidosis: A Case Report.

BACKGROUND: This case report intends to highlight the challenge in diagnosing type 1 diabetes on an ...

The ethical, legal and social implications of using artificial intelligence systems in breast cancer care.

Breast cancer care is a leading area for development of artificial intelligence (AI), with applicati...

Fetal Congenital Heart Disease Echocardiogram Screening Based on DGACNN: Adversarial One-Class Classification Combined with Video Transfer Learning.

Fetal congenital heart disease (FHD) is a common and serious congenital malformation in children. In...

Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening.

We present a deep convolutional neural network for breast cancer screening exam classification, trai...

IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge.

Diabetic Retinopathy (DR) is the most common cause of avoidable vision loss, predominantly affecting...

Clinical usefulness of a deep learning-based system as the first screening on small-bowel capsule endoscopy reading.

BACKGROUND AND AIM: To examine whether our convolutional neural network (CNN) system based on deep l...

Deep learning-enabled system for rapid pneumothorax screening on chest CT.

PURPOSE: Prompt diagnosis and quantitation of pneumothorax impact decisions pertaining to patient ma...

Predicting early risk of chronic kidney disease in cats using routine clinical laboratory tests and machine learning.

BACKGROUND: Advanced machine learning methods combined with large sets of health screening data prov...

[E-health and "Cancer outside the hospital walls", Big Data and artificial intelligence].

To heal otherwise in oncology has become an imperative of Public Health and an economic imperative i...

Building Risk Prediction Models for Type 2 Diabetes Using Machine Learning Techniques.

INTRODUCTION: As one of the most prevalent chronic diseases in the United States, diabetes, especial...

Automated Liver Fat Quantification at Nonenhanced Abdominal CT for Population-based Steatosis Assessment.

Background Nonalcoholic fatty liver disease and its consequences are a growing public health concern...

Artificial neural network metamodel for sensitivity analysis in a total hip replacement health economic model.

: Metamodels have been used to approximate complex simulations and have many applications with sensi...

Predicting Quality of Overnight Glycaemic Control in Type 1 Diabetes Using Binary Classifiers.

In type 1 diabetes management, maintaining nocturnal blood glucose within target range can be challe...

Machine Learning to Predict the Risk of Incident Heart Failure Hospitalization Among Patients With Diabetes: The WATCH-DM Risk Score.

OBJECTIVE: To develop and validate a novel, machine learning-derived model to predict the risk of he...

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