Health Policy

Use of a priority lane to increase voluntary visits to a milking robot in dairy cows.

Voluntary visits to the milking robot are the basis of automatic milking system functionality. Therefore, problems arise when cows are undermotivated to visit the robot. Cows with reduced competitive abilities, specifically those that are lame or low-ranking (or both), are at risk of lower visit frequencies. These cows typically have reduced autonomy as they must conform to the schedules of more d...

Machine learning center-specific models show improved IVF live birth predictions over US national registry-based model.

Expanding in vitro fertilization (IVF) access requires improved patient counseling and affordability via cost-success transparency. Clinicians ask how two types of live birth prediction (LBP) models perform: machine learning, center-specific (MLCS) models and the multicenter, US national registry-based model produced by Society for Assisted Reproductive Technology (SART). In a retrospective model ...

Developing a novel Temporal Air-quality Risk Index using LSTM autoencoder: A case study with South Korean air quality data.

As public awareness of environmental and health issues grows, providing accurate and accessible environmental risk information is essential for inform...

Hyperspectral Imaging and Deep Learning for Quality and Safety Inspection of Fruits and Vegetables: A Review.

Quality inspection of fruits and vegetables linked to food safety monitoring and quality control. Traditional chemical analysis and physical measureme...

Investigating long-term risk of aortic aneurysm and dissection from fluoroquinolones and the key contributing factors using machine learning methods.

The connection between fluoroquinolones and severe heart conditions, such as aortic aneurysm (AA) and aortic dissection (AD), has been acknowledged, b...

Assessing the Quality and Reliability of ChatGPT's Responses to Radiotherapy-Related Patient Queries: Comparative Study With GPT-3.5 and GPT-4.

BACKGROUND: Patients frequently resort to the internet to access information about cancer. However, these websites often lack content accuracy and rea...

Calibration and validation-based assessment of low-cost air quality sensors.

BACKGROUND: Air pollution poses a significant threat to public health. Low-cost air quality sensors (LCSs) can provide a data foundation for air quali...

Uncertainty-aware segmentation quality prediction via deep learning Bayesian Modeling: Comprehensive evaluation and interpretation on skin cancer and liver segmentation.

Image segmentation is a critical step in computational biomedical image analysis, typically evaluated using metrics like the Dice coefficient during t...

Extreme Weather, Vulnerable Populations, and Mental Health: The Timely Role of AI Interventions.

Environmental disasters are becoming increasingly frequent and severe, disproportionately impacting vulnerable populations who face compounded risks d...

The present and future of cardiological telemonitoring in Europe: a statement from seven European countries.

Cardiovascular diseases remain one of the leading causes of death worldwide, placing a significant burden on individuals, families and healthcare syst...

Transformer-based deep learning ensemble framework predicts autism spectrum disorder using health administrative and birth registry data.

Early diagnosis and access to resources, support and therapy are critical for improving long-term outcomes for children with autism spectrum disorder ...

Implementation of a national AI technology program on cardiovascular outcomes and the health system.

Coronary artery disease (CAD) is a major cause of ill health and death worldwide. Coronary computed tomographic angiography (CCTA) is the first-line i...

Predicting determinants of unimproved water supply in Ethiopia using machine learning analysis of EDHS-2019 data.

Over 2 billion people worldwide are impacted by the global dilemma of access to clean and safe drinking water. The problem is most acute in low-income...

A Novel Experience-Driven and Federated Intelligent Threat-Defense Framework in IoMT.

The Artificial Intelligence-enabled Internet of Medical Things (AI-IoMT) envisions the connectivity of medical devices encompassing advanced computing...

Brain tumor segmentation and detection in MRI using convolutional neural networks and VGG16.

BackgroundIn this research, we explore the application of Convolutional Neural Networks (CNNs) for the development of an automated cancer detection sy...

Learning-Based Models for Predicting IVIG Resistance and Coronary Artery Lesions in Kawasaki Disease: A Review of Technical Aspects and Study Features.

Kawasaki disease (KD) is a common pediatric vasculitis, with coronary artery lesions (CALs) representing its most severe complication. Early identific...

Exploring the intersection of brain-computer interfaces and traditional, complementary, and integrative medicine.

Brain-computer interfaces (BCIs) represent a transformative innovation in healthcare, enabling direct communication between the brain and external dev...

Impact of AI on Access to Care a Three-Pronged Approach for Enhancing Access to Surgical Care: Lessons From Electrical Safety and the Impact of AI on Health Equity.

As we enter the 21st century, a new wave of transformation is occurring in health care, mainly through artificial intelligence (AI). Like electricity,...

Establishment of an innovative machine learning-driven drinking water quality assessment model with health considerations.

With increasing resident incomes, public attention has increasingly focused on the health impact of drinking water. However, the development of evalua...

An effectiveness of machine learning models for estimate the financial cost of assistive services to disability care in the Kingdom of Saudi Arabia.

As per the World Health Organization (WHO), the justifications of people with disabilities globally are restricted by physical and social barriers tha...