Primary Care

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

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Immunometabolic alterations in type 2 diabetes mellitus revealed by single-cell RNA sequencing: insights into subtypes and therapeutic targets.

BACKGROUND: Type 2 Diabetes Mellitus (T2DM) represents a major global health challenge, marked by ch...

Predictive value of machine learning for the progression of gestational diabetes mellitus to type 2 diabetes: a systematic review and meta-analysis.

BACKGROUND: This systematic review aims to explore the early predictive value of machine learning (M...

Artificial intelligence-enhanced diagnosis of degenerative joint disease using temporomandibular joint panoramic radiography and joint noise data.

This study aimed to develop an artificial intelligence (AI) model for the screening of degenerative ...

Screening of obstructive sleep apnea and diabetes mellitus -related biomarkers based on integrated bioinformatics analysis and machine learning.

BACKGROUND: The pathophysiology of obstructive sleep apnea (OSA) and diabetes mellitus (DM) is still...

Early detection of canine hemangiosarcoma via cfDNA fragmentation and copy number alterations in liquid biopsies using machine learning.

Hemangiosarcoma is a highly malignant tumor commonly affecting canines, originating from endothelial...

Artificial intelligence in gastrointestinal cancers: Diagnostic, prognostic, and surgical strategies.

GI (Gastrointestinal) malignancies are one of the most common and lethal cancers globally. The dawn ...

Machine Learning-Assisted High-Throughput Screening of Nanozymes for Ulcerative Colitis.

Ulcerative colitis (UC) is a chronic gastrointestinal inflammatory disorder with rising prevalence. ...

AI image analysis as the basis for risk-stratified screening.

Artificial intelligence (AI) has emerged as a transformative tool in breast cancer screening, with t...

A benchmark of deep learning approaches to predict lung cancer risk using national lung screening trial cohort.

Deep learning (DL) methods have demonstrated remarkable effectiveness in assisting with lung cancer ...

Development of deep learning auto-encoder algorithms for predicting alcohol use in Korean adolescents based on cross-sectional data.

Alcohol is a highly addictive substance, presenting significant global public health concerns, parti...

AntiT2DMP-Pred: Leveraging feature fusion and optimization for superior machine learning prediction of type 2 diabetes mellitus.

Pancreatic α-amylase breaks down starch into isomaltose and maltose, which are further hydrolyzed by...

Predictive models and determinants of mortality among T2DM patients in a tertiary hospital in Ghana, how do machine learning techniques perform?

BACKGROUND: The increasing prevalence of type 2 diabetes mellitus (T2DM) in lower and middle - incom...

Integrating machine learning and structural dynamics to explore B-cell lymphoma-2 inhibitors for chronic lymphocytic leukemia therapy.

Chronic lymphocytic leukemia (CLL) is a malignancy caused by the overexpression of the anti-apoptoti...

Machine Learning Approach for Sepsis Risk Assessment in Ischemic Stroke Patients.

BackgroundIschemic stroke is a critical neurological condition, with infection representing a signif...

Enhancing stroke disease classification through machine learning models via a novel voting system by feature selection techniques.

Heart disease remains a leading cause of mortality and morbidity worldwide, necessitating the develo...

Machine learning and molecular dynamics simulations predict potential TGR5 agonists for type 2 diabetes treatment.

INTRODUCTION: Treatment of type 2 diabetes (T2D) remains a significant challenge because of its mult...

The effect of renal function on the clinical outcomes and management of patients hospitalized with hyperglycemic crises.

BACKGROUND: The global prevalence of diabetes has been rising rapidly in recent years, leading to an...

Machine learning-based analyses of contributing factors for the development of hypertension: a comparative study.

OBJECTIVES: Sufficient attention has not been given to machine learning (ML) models using longitudin...

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