Public Health & Policy

Health Policy

Latest AI and machine learning research in health policy for healthcare professionals.

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Prediction of Drug-Target Interactions With High- Quality Negative Samples and a Network-Based Deep Learning Framework.

Identification of drug-target interactions (DTIs) plays a crucial role in drug discovery. Compared t...

From NMR to AI: Do We Need H NMR Experimental Spectra to Obtain High-Quality logD Prediction Models?

This study presents a novel approach to H NMR-based machine learning (ML) models for predicting logD...

Advanced deep learning models for predicting elemental concentrations in iron ore mine using XRF data: a cost-effective alternative to ICP-MS methods.

Accurate elemental analysis is a critical requirement for mineral exploration, particularly in regio...

Opportunistic access control scheme for enhancing IoT-enabled healthcare security using blockchain and machine learning.

The healthcare industry, aided by technology, leverages the Internet of Things (IoT) paradigm to off...

Deep learning models for improving Parkinson's disease management regarding disease stage, motor disability and quality of life.

BACKGROUND AND OBJECTIVE: Motor diagnosis, monitoring and management of Parkinson's disease (PD) foc...

An quality evaluation method based on three-dimensional integration and machine learning: Advanced data processing.

This study presents an innovative approach for the quality evaluation of traditional Chinese medicin...

Developing a real-time water quality simulation toolbox using machine learning and application programming interface.

Rivers are vital for sustaining human life as they foster social development, provide drinking water...

Communication-Efficient Hybrid Federated Learning for E-Health With Horizontal and Vertical Data Partitioning.

Electronic healthcare (e-health) allows smart devices and medical institutions to collaboratively co...

Enhancing Ophthalmic Diagnosis and Treatment with Artificial Intelligence.

The integration of artificial intelligence (AI) in ophthalmology is transforming the field, offering...

Deep learning-driven prediction in healthcare systems: Applying advanced CNNs for enhanced breast cancer detection.

The mortality risk associated with breast cancer is experiencing an exponential rise, underscoring t...

Contribution of Structure Learning Algorithms in Social Epidemiology: Application to Real-World Data.

Epidemiologists often handle large datasets with numerous variables and are currently seeing a growi...

Effectiveness of AI for Enhancing Computed Tomography Image Quality and Radiation Protection in Radiology: Systematic Review and Meta-Analysis.

BACKGROUND: Artificial intelligence (AI) presents a promising approach to balancing high image quali...

Role of artificial intelligence in data-centric additive manufacturing processes for biomedical applications.

The role of additive manufacturing (AM) for healthcare applications is growing, particularly in the ...

Machine Learning Predicts Bleeding Risk in Atrial Fibrillation Patients on Direct Oral Anticoagulant.

Predicting major bleeding in nonvalvular atrial fibrillation (AF) patients on direct oral anticoagul...

Optimizing Quality Tolerance Limits Monitoring in Clinical Trials Through Machine Learning Methods.

The traditional clinical trial monitoring process, which relies heavily on site visits and manual re...

Quality assurance and validity of AI-generated single best answer questions.

BACKGROUND: Recent advancements in generative artificial intelligence (AI) have opened new avenues i...

Reporting Quality of AI Intervention in Randomized Controlled Trials in Primary Care: Systematic Review and Meta-Epidemiological Study.

BACKGROUND: The surge in artificial intelligence (AI) interventions in primary care trials lacks a s...

End-User Confidence in Artificial Intelligence-Based Predictions Applied to Biomedical Data.

Applications of Artificial Intelligence (AI) are revolutionizing biomedical research and healthcare ...

Prediction of school PM by an attention-based deep learning approach informed with data from nearby air quality monitoring stations.

Predicting indoor air pollutants concentrations in schools is essential for ensuring a healthy learn...

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