Primary Care

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

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Adaptive Wavelet Filters as Practical Texture Feature Amplifiers for Parkinson's Disease Screening in OCT

Parkinson's disease (PD) is a prevalent neurodegenerative disorder globally. The eye's retina is an extension of the brain and has great potential in PD screening. Recent studies have suggested that texture features extracted from retinal layers can be adopted as biomarkers for PD diagnosis under optical coherence tomography (OCT) images. Frequency domain learning techniques can enhance the feat...

The Role of Artificial Intelligence in Enhancing Insulin Recommendations and Therapy Outcomes

The growing worldwide incidence of diabetes requires more effective approaches for managing blood glucose levels. Insulin delivery systems have advanced significantly, with artificial intelligence (AI) playing a key role in improving their precision and adaptability. AI algorithms, particularly those based on reinforcement learning, allow for personalised insulin dosing by continuously adapting ...

Automated diagnosis of lung diseases using vision transformer: a comparative study on chest x-ray classification

Background: Lung disease is a significant health issue, particularly in children and elderly individuals. It often results from lung infections and ...

Preferential Multi-Objective Bayesian Optimization for Drug Discovery

Despite decades of advancements in automated ligand screening, large-scale drug discovery remains resource-intensive and requires post-processing hi...

Mapping intellectual structure and research hotspots of cancer studies in primary health care: A machine-learning-based analysis.

In the contemporary fight against cancer, primary health care (PHC) services hold a significant and critical position within the healthcare system. Th...

Mar 21 2025 40128045
Novel AI-Based Quantification of Breast Arterial Calcification to Predict Cardiovascular Risk

Women are underdiagnosed and undertreated for cardiovascular disease. Automatic quantification of breast arterial calcification on screening mammogr...

Advancing Chronic Tuberculosis Diagnostics Using Vision-Language Models: A Multi modal Framework for Precision Analysis

Background: This study proposes a Vision-Language Model (VLM) leveraging the SIGLIP encoder and Gemma-3b transformer decoder to enhance automated ch...

Subgroup Performance of a Commercial Digital Breast Tomosynthesis Model for Breast Cancer Detection

While research has established the potential of AI models for mammography to improve breast cancer screening outcomes, there have not been any detai...

Machine Learning-Based Model for Postoperative Stroke Prediction in Coronary Artery Disease

Coronary artery disease remains one of the leading causes of mortality globally. Despite advances in revascularization treatments like PCI and CABG,...

Optimizing Large Language Models for Detecting Symptoms of Comorbid Depression or Anxiety in Chronic Diseases: Insights from Patient Messages

Patients with diabetes are at increased risk of comorbid depression or anxiety, complicating their management. This study evaluated the performance ...

Mitochondrial mt12361A>G increased risk of metabolic dysfunction-associated steatotic liver disease among non-diabetes.

BACKGROUND: Insulin resistance, lipotoxicity, and mitochondrial dysfunction contribute to the pathogenesis of metabolic dysfunction-associated steatot...

Mar 14 2025 40093674
BioSerenity-E1: a self-supervised EEG model for medical applications

Electroencephalography (EEG) serves as an essential diagnostic tool in neurology; however, its accurate manual interpretation is a time-intensive pr...

Comprehensive Benchmarking of Machine Learning Methods for Risk Prediction Modelling from Large-Scale Survival Data: A UK Biobank Study

Predictive modelling is vital to guide preventive efforts. Whilst large-scale prospective cohort studies and a diverse toolkit of available machine ...

Analysis of 3D Urticaceae Pollen Classification Using Deep Learning Models

Due to the climate change, hay fever becomes a pressing healthcare problem with an increasing number of affected population, prolonged period of aff...

Precise Insulin Delivery for Artificial Pancreas: A Reinforcement Learning Optimized Adaptive Fuzzy Control Approach

This paper explores the application of reinforcement learning to optimize the parameters of a Type-1 Takagi-Sugeno fuzzy controller, designed to ope...

Gaussian Random Fields as an Abstract Representation of Patient Metadata for Multimodal Medical Image Segmentation

The growing rate of chronic wound occurrence, especially in patients with diabetes, has become a concerning trend in recent years. Chronic wounds ar...

A Protocol to Exposure Path Analysis for Multiple Stressors Associated with Cardiovascular Disease Risk: A Novel Approach Using NHANES Data

Background: Multiple medical and non-medical stressors, along with the complicity of their exposure pathways, have posted significant challenges to ...

A Comparative Study of Diabetes Prediction Based on Lifestyle Factors Using Machine Learning

Diabetes is a prevalent chronic disease with significant health and economic burdens worldwide. Early prediction and diagnosis can aid in effective ...

Increase Docking Score Screening Power by Simple Fusion With CNNscore.

Scoring functions (SFs) of molecular docking is a vital component of structure-based virtual screening (SBVS). Traditional SFs yield their inherent sh...

Mar 5 2025 39981784
Multimodal AI predicts clinical outcomes of drug combinations from preclinical data

Predicting clinical outcomes from preclinical data is essential for identifying safe and effective drug combinations. Current models rely on structu...

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