Health Policy

Machine-learning-based cost prediction models for inpatients with mental disorders in China.

BACKGROUND: Mental disorders are increasingly prevalent, leading to increased medical expenditures. To refine the reimbursement of medical costs for inpatients with mental disorders by health insurance, an accurate prediction model is essential. Per-diem payment is a common internationally implemented payment method for medical insurance of inpatients with mental disorders, necessitating the explo...

Machine learning-based automated waste sorting in the construction industry: A comparative competitiveness case study.

This article presents a comparative analysis of the circularity and cost-efficiency of two distinct construction material recycling processes: ML-based automated sorting (MLAS) and conventional sorting technologies. Empirical data was collected from two Finnish companies, providing a robust foundation for this comparison. Our study examines the operational specifics, economic implications, and env...

The Goldilocks Zone: Finding the right balance of user and institutional risk for suicide-related generative AI queries.

Generative artificial intelligence (genAI) has potential to improve healthcare by reducing clinician burden and expanding services, among other uses. ...

Deep Power-Aware Tunable Weighting for Ultrasound Microvascular Imaging.

Ultrasound microvascular imaging (UMI), including ultrafast power Doppler imaging (uPDI) and ultrasound localization microscopy (ULM), obtains blood f...

Semantic segmentation using synthetic images of underwater marine-growth.

INTRODUCTION: Subsea applications recently received increasing attention due to the global expansion of offshore energy, seabed infrastructure, and ma...

Revolutionizing surgery: AI and robotics for precision, risk reduction, and innovation.

Artificial intelligence and robotics are revolutionizing surgical practices by enhancing precision, efficiency, and patient outcomes. With global heal...

Countering AI-powered disinformation through national regulation: learning from the case of Ukraine.

Advances in the use of AI have led to the emergence of a greater variety of forms disinformation can take and channels for its proliferation. In this ...

AQuA-P: A machine learning-based tool for water quality assessment.

This study addresses the critical challenge of assessing the quality of groundwater and surface water, which are essential resources for various socie...

A robust and interpretable ensemble machine learning model for predicting healthcare insurance fraud.

Healthcare insurance fraud imposes a significant financial burden on healthcare systems worldwide, with annual losses reaching billions of dollars. Th...

Bridging MRI Cross-Modality Synthesis and Multi-Contrast Super-Resolution by Fine-Grained Difference Learning.

In multi-modal magnetic resonance imaging (MRI), the tasks of imputing or reconstructing the target modality share a common obstacle: the accurate mod...

Predictive performance of count regression models versus machine learning techniques: A comparative analysis using an automobile insurance claims frequency dataset.

Accurate forecasting of claim frequency in automobile insurance is essential for insurers to assess risks effectively and establish appropriate pricin...

Fraud detection in healthcare claims using machine learning: A systematic review.

OBJECTIVE: Identifying fraud in healthcare programs is crucial, as an estimated 3%-10% of the total healthcare expenditures are lost to fraudulent act...

Enhancing early detection of Alzheimer's disease through hybrid models based on feature fusion of multi-CNN and handcrafted features.

Alzheimer's disease (AD) is a brain disorder that causes memory loss and behavioral and thinking problems. The symptoms of Alzheimer's are similar thr...

Adoption of Digital Therapeutics in Europe.

Digital therapeutics (DTx) are an emerging medical therapy comprising evidence-based interventions that are regulatory approved for patient use, or ar...

SymScore: Machine learning accuracy meets transparency in a symbolic regression-based clinical score generator.

Self-report questionnaires play a crucial role in healthcare for assessing disease risks, yet their extensive length can be burdensome for respondents...

Assessing the accuracy and quality of artificial intelligence (AI) chatbot-generated responses in making patient-specific drug-therapy and healthcare-related decisions.

BACKGROUND: Interactive artificial intelligence tools such as ChatGPT have gained popularity, yet little is known about their reliability as a referen...

A prospective study comparing highly qualified Molecular Tumor Boards with AI-powered software as a medical device.

BACKGROUND: The implementation of cancer precision medicine in Japan is deeply intertwined with insurance reimbursement policies and requires case-by-...

Artificial intelligence and sexual reproductive health and rights: a technological leap towards achieving sustainable development goal target 3.7.

Target 3.7 of the Sustainable Development Goals (SDGs) aims for universal access to sexual and reproductive health (SRH) services by 2030, including f...

Machine Learning for Predicting Zearalenone Contamination Levels in Pet Food.

Zearalenone (ZEN) has been detected in both pet food ingredients and final products, causing acute toxicity and chronic health problems in pets. There...

Cost-Utility Analysis of Add-on Cannabidiol vs Usual Care Alone for the Treatment of Seizures in Patients With Treatment-Resistant Lennox-Gastaut Syndrome or Dravet Syndrome in the Netherlands.

Lennox-Gastaut syndrome (LGS) and Dravet syndrome (DS) are severe, treatment-refractory, epileptic encephalopathies that often develop in infancy or ...