Latest AI and machine learning research in medicare for healthcare professionals.
BACKGROUND: While medications are essential for preventing and treating disease, they can also cause harm. Evidence synthesis has been widely adopted for evaluating harms, but traditional methods are resource-intensive and may constrain timely decision-making. This study aims to validate a Trial Bank approach towards rapid evidence synthesis. METHODS: A Trial Bank consisting of 13,650 RCTs of phar...
PURPOSE: Ensemble machine learning (ML) demonstrated potential for improving predictions based on big health care data. We developed and validated interpretable ensemble ML models in evaluating the nonadherence and nonpersistence of biological or targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs) in rheumatoid arthritis (RA). METHODS: This retrospective study used 5% Medicare cl...
OBJECTIVE: This study investigated that serum metabolomics, prior to ULT initiation, could serve as a biomarker for responsiveness to colchicine proph...
Graph Neural Networks (GNNs) are routinely used in molecular physics, social sciences, and economics to model many-body interactions in graph-like sys...
Our study investigates the effects of long-duration spaceflight on brain aging in spacefarers using structural MRI and machine learning models. Pre-, ...
BACKGROUND: Automated approaches to cognitive impairment screening may soon achieve sufficient levels of accuracy for clinical implementation but they...
The lack of long-range electrostatics is a key limitation of modern machine learning interatomic potentials (MLIPs), hindering reliable applications t...
Device-to-device (D2D) communication is used to frequently gather and exchange information in various domains. Millimeter-wave research has also incor...
BACKGROUND: Chemoradiation therapy (CRT) is standard treatment for anal squamous cell carcinoma (ASCC). Abdominoperineal resection (APR) is usually re...
This study investigates global news media representations of cross-Strait relations from 2014 to 2023 using the GDELT database, framed within the medi...
BACKGROUND: Interstitial fibrosis (IF) is the strongest predictor of chronic kidney disease progression. Visual estimation of IF from trichrome (TRI)-...
Systemic barriers, including language, navigation complexity, and long specialist wait-times, result in the under-utilization of mental health service...
INTRODUCTION: High costs of screening and diagnostic tests remain a major barrier to timely tuberculosis (TB) identification in resource-limited setti...
Artificial intelligence-based in silico metabolite prediction tools are increasingly used in drug development, but their performance against high-qual...
BACKGROUND: Psychological distress, particularly symptoms of depression and anxiety (D&A), is highly prevalent among family caregivers of individuals ...
The rapid advancement of deep learning has intensified the need for comprehensive data for use by autonomous driving algorithms. High-quality datasets...
BACKGROUND: Large language models (LLMs) have fundamentally transformed approaches to natural language processing tasks across diverse domains. In hea...
IgA nephropathy (IgAN) is the most prevalent primary glomerular disease worldwide and a leading cause of end-stage kidney disease (ESKD). Its clinical...
Video datasets are crucial for advancing communication technologies for deaf and hard-of-hearing individuals. Despite that, extensive datasets are not...
The integration of ProSocial Artificial Intelligence (AI) represents a paradigm shift in prosthodontics, harmonizing cutting-edge technology with ethi...