Primary Care

Naturalistic driving data can identify elevated driving risk in adults with Type 1 diabetes.

OBJECTIVE: Individuals with Type 1 Diabetes Mellitus (T1DM) may experience acute glucose events, such as hypoglycemia and severe hyperglycemia, that can impair attention, reaction time, and decision-making during activities of daily living (ADL), such as driving. While continuous glucose monitoring (CGM) detects such events, less is known about whether everyday driving behavior reflects days when ...

Decoding the molecular memory of obesity using machine learning and microRNA dynamics.

Multiscale attribution of atmospheric methane variability in China using satellite observations and interpretable machine learning.

Methane is one of the primary anthropogenic greenhouse gases influencing near-term climate warming. Atmospheric concentrations of methane continue to ...

Metabolomic and lifestyle profiles refine BMI-metabolic phenotypes in older adults.

Conventional BMI-based classifications, even when combined with traditional cardiometabolic risk factors, limit precision in aging-related risk assess...

User profiles of young breast cancer survivors on Chinese social media: machine learning-based text mining analysis study.

PURPOSE: Social media is vital for improving healthcare access, especially among disadvantaged groups. During the COVID-19 pandemic, young breast canc...

Decision tree-based prediction of 5-year weight trajectories after bariatric surgery in adolescents and young adults: a retrospective cohort study from France and Sweden.

BACKGROUND: Metabolic-bariatric surgery is an efficient therapy in selected adolescents with severe obesity. However, predicting the postoperative wei...

Data Augmentation via Digital Twins to Develop Personalized Deep Learning Glucose Prediction Algorithms for Type 1 Diabetes in Poor Data Context.

OBJECTIVE: Accurately predicting glucose levels is essential for effectively managing type 1 diabetes (T1D), a chronic condition in which the body can...

Predicting the Complex, Multilevel Ecology of US County-Level Diabetes Prevalence: A Cross-Sectional, Artificial Intelligence Analysis.

BACKGROUND: Diabetes and its risk factors are embedded in a complex multilevel ecology. Upstream factors (i.e., 'forcing factors' that refer to fundam...

Using Natural Language Processing of Clinical Notes to Supplement Structured Electronic Health Record Data for Phenotyping Smoking and Obesity in a Healthcare System.

PURPOSE: Studies based on electronic health records (EHR) often rely on structured data, which may incompletely capture important clinical phenotypes ...

Protocol-Based Management of Medication Confounders in Brain Death Determination: A Multi-Institutional Review.

BACKGROUND: Determination of death by neurologic criteria (DNC) relies on clinical examination, provided no confounding conditions affect reliability....

[The application of artificial intelligence in arrhythmology].

In recent decades, clinical practice has been founded on the principles of evidence-based medicine, where therapeutic decisions arise from the integra...

Time-dependent predictive contribution of the triglyceride-glucose index for incident cardiovascular disease in a 9-year Chinese cohort.

This study aims to investigate the association between triglyceride-glucose (TyG) index and risk of CVD using explainable survival analysis method bas...

Bone Mineral Density Loss After Kidney Transplantation: Metabolic Determinants, Diagnostic Approaches, and Preventive Strategies.

Mineral and bone disease (MBD) is a frequent complication after solid organ transplantation. The rapid decline in bone mineral density (BMD) during th...

Artificial intelligence for cervical cancer screening and diagnosis using Pap smear images: a systematic review.

BACKGROUND: Cervical cancer continues to be the second most prevalent type of cancer in women globally, especially in the less developed areas. Among ...

Risk prediction of sepsis-associated acute kidney injury: development, validation of a machine learning model with multicenter data.

BACKGROUND: Sepsis-associated acute kidney injury (SA-AKI) is a common and severe complication in intensive care unit (ICU) patients, with high incide...

Automated diabetic retinopathy grading and screening using deep learning.

PURPOSE: To develop and benchmark a unified Deep Learning (DL) pipeline for automated detection and five-level grading of Diabetic Retinopathy (DR), a...

Screening of Medicinal and Edible Homology Substances for Diabetic Kidney Disease Based on GraphBAN.

BACKGROUND: Diabetic kidney disease (DKD) is a critical microvascular complication of diabetes mellitus, and the current pharmacotherapies are limited...

Machine learning-based prediction model for adverse pregnancy outcomes in women with gestational diabetes mellitus.

AIMS/INTRODUCTION: Gestational diabetes mellitus (GDM) is one of the most frequent pregnancy complications. Investigating clinical risk factors for ad...

Low-burden data-driven work addiction screening: Comparing demographic and psychosocial predictors using machine learning.

BACKGROUND AND AIMS: Identifying employees at risk for work addiction is critical for early intervention, yet systematic screening approaches face pra...

Molecular mechanisms of erectile dysfunction in type 1 and type 2 diabetic rats: a multiomics approach.

Erectile dysfunction (ED) is a prevalent complication of both type 1 and type 2 diabetes mellitus (DM), but the shared molecular mechanisms underlying...