Primary Care

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

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Which approach better predicts diabetes: Traditional econometric methods or machine learning? Evidence from a cross-sectional study in South Korea.

To prevent chronic disease from getting worse, it is important to detect and predict it at an early ...

Learning from the machine: is diabetes in adults predicted by lifestyle variables? A retrospective predictive modelling study of NHANES 2007-2018.

OBJECTIVES: This study aimed to compare the performance of five machine learning algorithms to predi...

Population Health in Neurology and the Transformative Promise of Artificial Intelligence and Large Language Models.

This manuscript examines the expanding role of population health strategies in neurology, emphasizin...

Machine learning-based risk prediction model for neuropathic foot ulcers in patients with diabetic peripheral neuropathy.

BACKGROUND: Diabetic peripheral neuropathy (DPN) is a common chronic complication of diabetes, marke...

Personalized Blood Glucose Forecasting From Limited CGM Data Using Incrementally Retrained LSTM.

For people with Type 1 diabetes (T1D), accurate blood glucose (BG) forecasting is crucial for the ef...

A diagnostic model for polycystic ovary syndrome based on machine learning.

Diagnosis of polycystic ovary syndrome remains a challenge. In this study, we propose constructing a...

Artificial intelligence system for predicting hand-foot skin reaction induced by vascular endothelial growth factor receptor inhibitors.

Hand-foot skin reaction (HFSR) is a common adverse effect of vascular endothelial growth factor rece...

RNAmigos2: accelerated structure-based RNA virtual screening with deep graph learning.

RNAs are a vast reservoir of untapped drug targets. Structure-based virtual screening (VS) identifie...

The relationship between epigenetic biomarkers and the risk of diabetes and cancer: a machine learning modeling approach.

INTRODUCTION: Epigenetic biomarkers are molecular indicators of epigenetic changes, and some studies...

Unveiling CNS cell morphology with deep learning: A gateway to anti-inflammatory compound screening.

Deciphering the complex relationships between cellular morphology and phenotypic manifestations is c...

Optimizing deep learning models for glaucoma screening with vision transformers for resource efficiency and the pie augmentation method.

Glaucoma is the leading cause of irreversible vision impairment, emphasizing the critical need for e...

A Review of In Silico Approaches for Discovering Natural Viral Protein Inhibitors in Aquaculture Disease Control.

Viral diseases pose a significant threat to the sustainability of global aquaculture, causing econom...

Automated Detection of Microcracks Within Second Harmonic Generation Images of Cartilage Using Deep Learning.

Articular cartilage, essential for smooth joint movement, can sustain micrometer-scale microcracks i...

Machine learning applications to classify and monitor medication adherence in patients with type 2 diabetes in Ethiopia.

BACKGROUND: Medication adherence plays a crucial role in determining the health outcomes of patients...

Emerging Applications of Digital Technologies for Periodontal Screening, Diagnosis and Prognosis in the Dental Setting.

AIM: To comprehensively review digital technologies (including artificial intelligence, AI) for peri...

Predicting intra-abdominal hypertension using anthropometric measurements and machine learning.

Almost one in four critically ill patients suffer from intra-abdominal hypertension (IAH). Currently...

Machine learning reveals distinct neuroanatomical signatures of cardiovascular and metabolic diseases in cognitively unimpaired individuals.

Comorbid cardiovascular and metabolic risk factors (CVM) differentially impact brain structure and i...

Multitask learning model for predicting non-coding RNA-disease associations: Incorporating local and global context.

Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are crucial non-coding RNAs involved in variou...

: Towards Autonomous Electronic Health Record Navigation.

Clinicians spend large amounts of time on clinical documentation, and inefficiencies impact quality ...

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