Primary Care

Gene-Lifestyle Synergy in Diabetic Retinopathy Development.

PURPOSE: To develop a machine learning (ML)-driven polygenic risk score (PRS) for diabetic retinopathy (DR) and evaluate the extent to which lifestyle may mitigate genetic risk. DESIGN: Multicenter, multiethnic cohort study. PARTICIPANTS: 9 1691 participants with DR-free prediabetes/diabetes from the UK Biobank (UKB); and 1119 participants with DR-free diabetes from the Guangzhou Diabetic Eye Stud...

Machine Learning Unveils Dietary Antioxidants as Influential Factors for Diabetes-Cancer Comorbidity: Insights From National Health and Nutrition Examination Survey.

This study aims to assess the predictive value of dietary antioxidants in diabetes-cancer comorbidity using interpretable machine learning (ML) models and to identify key clinical factors. Data were sourced from the National Health and Nutrition Examination Survey (NHANES) 2007-2010 and 2017-2018 cycles, including 44 dietary antioxidants, as well as demographic, lifestyle, and health-related featu...

Advances in Digital Cytopathology and Artificial Intelligence Applications.

Cytopathology is the first field of pathology in which artificial intelligence (AI) models were successfully developed and commercialized for routine ...

Predicting abdominal obesity in children and adolescents via machine learning: a longitudinal cohort study.

OBJECTIVES: This study aimed to construct and verify machine learning (ML) models to predict long-term abdominal obesity (AO) risk in children and ado...

Artificial Intelligence Applications across the Spectrum of Malnutrition: From Undernutrition to Obesity.

BACKGROUND: Malnutrition is a significant global public health challenge, with rising prevalence and vital consequences. Recent advances in artificial...

The needle study: Machine learning as a new method for case-finding in celiac disease.

OBJECTIVES: Despite a well-defined diagnostic work-up, uncertainties persist regarding celiac disease (CeD) detection strategies in the general popula...

Integration of Continuous Glucose Monitoring With HbA1c to Improve the Detection of Prediabetes in Asian Individuals: Model Development Study.

BACKGROUND: Glycated hemoglobin (HbA1c) is a convenient tool to evaluate glycemic status but its ability to detect individuals at risk for type 2 diab...

Machine Learning-Derived Cardiovascular Aging Phenotypes From Cardiac Function and Stroke Risk in the UK Biobank: Cohort Study.

BACKGROUND: Cardiovascular magnetic resonance (CMR) is widely used across various cardiac conditions and systematically assesses cardiac anatomical st...

A hybrid feature selection framework combining Artificial Bee Colony and decision trees for CVD risk assessment.

Cardiovascular Disease (CVD) risk assessment involves evaluating various clinical and lifestyle factors to estimate an individual's likelihood of deve...

Predicting primary aldosteronism: risk stratification-based and machine learning-based models.

AIMS: Primary aldosteronism (PA) screening is difficult in an unselected population of hypertensive patients, as it can be challenging. We report new ...

Establishing a Diagnostic Model Based on Plasma CDR1as and Alpha-Synuclein for Mild Cognitive Impairment in Type 2 Diabetes.

Emerging evidence implicates cerebellar degeneration-related protein 1 antisense transcript (CDR1as) and alpha-synuclein (α-Syn) in diabetes and cogni...

Biomarker-anchored screening of dietary microplastics via a gut microbiota-informed machine learning model.

Microplastics (MPs) are increasingly recognized as emerging contaminants in the human diet, yet the absence of unified biomarker-anchored screening th...

Algometric assessment of sensitization and habituation as predictive markers for OnabotulinumtoxinA response in chronic migraine.

BACKGROUND: Chronic migraine is a debilitating disorder characterized by central sensitization and impaired habituation. Although OnabotulinumtoxinA (...

MetaboNet: The Largest Publicly Available Consolidated Data Set for Type 1 Diabetes Management.

BACKGROUND: Progress in type 1 diabetes (T1D) algorithm development is limited by the fragmentation and lack of standardization across existing T1D ma...

Identifying Actionable Glucose Patterns in Type 1 and Type 2 Diabetes Using Continuous Glucose Monitoring and Algorithmic Pattern Recognition.

BACKGROUND: Continuous glucose monitoring (CGM) provides real-time glucose data, aiding diabetes management. Identifying glucose patterns is difficult...

Artificial intelligence-assisted design of Chinese herbal medicine based hydrogels.

The rapid development of artificial intelligence (AI) and the increasing demand for the modernization of Chinese herbal medicine (CHM) have created ne...

Evaluation of large language models for carbohydrate counting in type 1 diabetes mellitus: a comparative study with patient estimations.

AIMS: To evaluate large language models (LLMs) accuracy in carbohydrate (CHO) counting (CC) and compare their performance with estimations by patients...

Lactate-albumin ratio predicts in-hospital mortality in critically Ill patients with congestive heart failure and diabetes.

OBJECTIVE: To evaluate the predictive utility of the initial lactate-to-albumin ratio (LAR) measured within 24 h of admission for in-hospital all-caus...

Increasing value in the Veterans Affairs Healthcare System (VA) with precision health: a continuing landmark collaboration with the Department of Energy.

OBJECTIVE: Phase II of MVP-CHAMPION, a federal collaboration between the Veterans Affairs Healthcare System (VA) and the Department of Energy (DoE), l...

Dementia screening with the Rivermead Behavioural Memory Test: A machine learning analysis.

BackgroundWith the advent of anti-amyloid-β monoclonal antibody therapies and the growing societal burden of dementia, early identification of Alzheim...