Primary Care

Inferring high-fat dietary patterns from electronic health record data using machine learning.

OBJECTIVES: Electronic health records (EHRs) rarely capture dietary detail, limiting diet-disease research. We aimed to develop machine learning (ML) computable phenotypes to identify high-fat diet (HFD) using variables typically available in EHRs. MATERIALS AND METHODS: We used National Health and Nutrition Examination Survey (NHANES) 1999-2020 data, where 24-h dietary recall served as ground tru...

A risk model to predict atrial fibrillation in diabetes using machine learning: The ACCORD study.

BACKGROUND: Machine learning (ML) may improve prediction of atrial fibrillation (AF), but its value compared with traditional models such as Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE-AF) in patients with diabetes remains unclear. METHODS: Among 9,307 patients in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) with type 2 diabetes and no prior AF, a random ...

Quantitative evaluation of retinal vascular morphology based on the human visual bionic mechanism for the evaluation of diabetic retinopathy onset.

BACKGROUND AND AIMS: A limited amount of diabetic retinopathy (DR) development can be explained by traditional risk factors. This study aimed to deter...

Integrating probabilistic trees and causal networks for clinical and epidemiological data.

Healthcare decision-making requires not only accurate predictions but also insights into how factors influence patient outcomes. While traditional mac...

[Chronic skin damage from UV radiation].

Ultraviolet (UV) radiation is the primary risk factor for the development of both melanocytic and nonmelanocytic skin cancer. In particular, UVA and U...

Examining attention- deficit/ hyperactivity disorder in endurance and ultra-endurance runners.

BACKGROUND: Attention-deficit hyperactivity disorder (ADHD) is a multifactorial and complex neurodevelopmental disorder. Prevalence of ADHD in the gen...

Discovery of PPARγ Partial Agonists for Treatment of Type 2 Diabetes Based on an Integrated Virtual Screening Strategy that Combines Fragment Molecular Orbital Calculations, Machine Learning, Molecular Docking, Interaction Fingerprint Filtering, and Molecular Dynamics Simulations.

Peroxisome proliferator-activated receptor γ (PPARγ) is a key therapeutic target for type 2 diabetes and cardiovascular diseases due to its central ro...

The role of artificial intelligence in hypertension management.

PURPOSE OF REVIEW: Hypertension remains a leading modifiable risk factor for cardiovascular and renal conditions and dementia. Given its rising global...

SMARTDock: A Toolkit for the Automated Development of Target-Specific Scoring Functions Using Bioactivity Data.

Molecular docking has become an essential tool in the early stages of structure-based drug discovery, enabling rapid virtual screening of large compou...

The effects of e-Health and artificial intelligence literacy levels on disease self-management in patients with diabetes.

BACKGROUND: This study was conducted to examine the effects of eHealth and artificial intelligence literacy on disease self-management in patients wit...

Construction and validation of a screening model for minimal hepatic encephalopathy in patients with cirrhosis: A multi-center study.

BACKGROUND: Clinical practice currently lacks objective and accurate screening tools for minimal hepatic encephalopathy (MHE). Therefore, we aimed to ...

Imaging Evaluation for Steatotic Liver Disease.

Metabolic dysfunction-associated steatotic liver disease (MASLD), previously known as nonalcoholic fatty liver disease, is the fastest-growing cause o...

Acceleration of chemical shift encoding-based water-fat imaging for pancreatic proton density fat fraction mapping in a single breath-hold: Data from the LION study.

PURPOSE: With the rising prevalence of obesity and metabolic syndrome, there is an increasing need for noninvasive quantification of pancreatic fat as...

DNA-Encoded Chemical Library Screening with Target Titration Analysis: DELTA.

DNA-encoded chemical libraries (DELs) enable the highly efficient screening of billions of small molecules for binding to a target of interest and pro...

Inaccurate information regarding cardiovascular disease prevention enabled by generative artificial intelligence.

Inaccurate information regarding cardiovascular disease (CVD) prevention is prevalent on the internet and may influence medical decisions. Artificial ...

Integrating AI-ECG and point-of-care cardiac ultrasound for screening structural heart disease: A proof-of-concept study.

BACKGROUND: Early structural heart disease (SHD) detection is crucial for improving prognostic outcomes, but widely accessible screening methods are l...

Beneficial effect of animal skin-derived bioactive peptides on type 2 diabetes mellitus: A review on preparation, structure-activity relationship, and blood glucose regulation mechanism.

Type 2 diabetes mellitus (T2DM) is a global disease threatening human health. Regulating blood glucose homeostasis is a key strategy for the treatment...

Molecular mechanism and structure-activity relationships of natural source polysaccharides in intervening type 2 diabetes mellitus through antioxidant effects: A systematic review.

Type 2 diabetes mellitus (T2DM) is a prevalent metabolic disorder closely associated with oxidative stress. Natural source polysaccharides (NSPs) show...

Performance of an automated Korotkoff sound BP monitor in cardiovascular patients.

OBJECTIVES: Accurate blood pressure measurement is essential for cardiovascular risk management, but conventional oscillometric devices are unreliable...

Factors Impacting the Performance of Deep Learning Detection of Pulmonary Emboli.

OBJECTIVE: AI models are increasingly adopted in clinical practice, yet their generalizability outside controlled validation settings remains unclear....