Primary Care

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

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Machine learning of clinical phenotypes facilitates autism screening and identifies novel subgroups with distinct transcriptomic profiles.

Autism spectrum disorder (ASD) presents significant challenges in diagnosis and intervention due to ...

Identification of patients at risk for pancreatic cancer in a 3-year timeframe based on machine learning algorithms.

Early detection of pancreatic cancer (PC) remains challenging largely due to the low population inci...

Prioritization strategies for non-target screening in environmental samples by chromatography - High-resolution mass spectrometry: A tutorial.

Non-target screening (NTS) using chromatography coupled to high-resolution mass spectrometry (HRMS),...

HEPOM: Using Graph Neural Networks for the Accelerated Predictions of Hydrolysis Free Energies in Different pH Conditions.

Hydrolysis is a fundamental family of chemical reactions where water facilitates the cleavage of bon...

An effective PO-RSNN and FZCIS based diabetes prediction and stroke analysis in the metaverse environment.

Chronic disease (CD) like diabetes and stroke impacts global healthcare extensively, and continuous ...

A quantum inspired machine learning approach for multimodal Parkinson's disease screening.

Parkinson's disease, currently the fastest-growing neurodegenerative disorder globally, has seen a 5...

Responsible CVD screening with a blockchain assisted chatbot powered by explainable AI.

Cardiovascular disease (CVD) is rising as a significant concern for the healthcare sector around the...

Deep learning prediction of mammographic breast density using screening data.

This study investigated a series of deep learning (DL) models for the objective assessment of four c...

DEMENTIA: A Hybrid Attention-Based Multimodal and Multi-Task Learning Framework With Expert Knowledge for Alzheimer's Disease Assessment From Speech.

The prevalence of Alzheimer's disease (AD) is rising annually, imposing a severe burden on patients ...

Early obesity risk prediction via non-dietary lifestyle factors using machine learning approaches.

Obesity poses a significant health threat, contributing to the development of noncommunicable diseas...

Predicting the risk of ischemic stroke in patients with atrial fibrillation using heterogeneous drug-protein-disease network-based deep learning.

Current risk assessment models for predicting ischemic stroke (IS) in patients with atrial fibrillat...

Clinical implementation of AI-based screening for risk for opioid use disorder in hospitalized adults.

Adults with opioid use disorder (OUD) are at increased risk for opioid-related complications and rep...

Generating evidence to support the role of AI in diabetic eye screening: considerations from the UK National Screening Committee.

Screening for diabetic retinopathy has been shown to reduce the risk of sight loss in people with di...

Discovery and Characterization of Novel Receptor-Interacting Protein Kinase 1 Inhibitors Using Deep Learning and Virtual Screening.

Receptor-interacting protein kinase 1 (RIPK1) serves as a critical mediator of cell necroptosis and ...

Machine learning models to predict osteoporosis in patients with chronic kidney disease stage 3-5 and end-stage kidney disease.

Chronic kidney disease-mineral bone disorder is a common complication in patients with chronic kidne...

Personalized glucose forecasting for people with type 1 diabetes using large language models.

BACKGROUND AND OBJECTIVE: Type 1 Diabetes (T1D) is an autoimmune disease that requires exogenous ins...

Integrative Multi-Omics and Routine Blood Analysis Using Deep Learning: Cost-Effective Early Prediction of Chronic Disease Risks.

Chronic noncommunicable diseases (NCDS) are often characterized by gradual onset and slow progressio...

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