Latest AI and machine learning research in medicare for healthcare professionals.
Artificial Intelligence (AI) offers potential to support and empower nurses, yet its development depends on the availability of high-quality, standardized data. Nursing data are often fragmented, unstructured, and semantically inconsistent, hindering their secondary use. This work aims to harmonize heterogeneous nursing data from hospitals and nursing homes to create an AI-ready data foundation. F...
This study presents and evaluates an automated volumetric modulated arc therapy (VMAT) planning framework for breast cancer based on objective function value (OFV)-guided optimization. The primary objective is to systematically improve organ-at-risk sparing through automated and reproducible optimization of planning constraints while maintaining clinically acceptable target coverage. An OFV-guided...
Genotoxicity assessment is crucial for drug development and chemical safety evaluation. However, traditional experimental approaches are time-consumin...
BACKGROUND: Early and reliable grading of diabetic retinopathy is important for preventing avoidable vision loss. Although deep learning methods have ...
Artificial intelligence (AI) is transforming medicine by changing how clinicians learn, reason, communicate, and care for patients. Clinician expertis...
Controlling water structure and dynamics at silica interfaces are central to a wide range of technologies, including protective oxide layers for solar...
UNLABELLED: Immunization is one of the most effective interventions to prevent infectious diseases. Identifying individuals at risk of non-adherence t...
BACKGROUND: Vaccination remains one of the most effective public health tools globally. Yet recent years have seen increasing political polarization a...
BACKGROUND AND OBJECTIVES: Adults with sickle cell disease (SCD) are at risk of decline in brain health and cognition, even without clinical stroke. S...
Humans are remarkable in their ability to quickly learn to perform complex tasks. Reinforcement learning (RL) has long been proposed as a model of hum...
BACKGROUND: Clinical notes are the most abundant data type within electronic health records; however, their highly unstructured format presents signif...
BACKGROUND: Eligibility criteria are essential to clinical trial design, guiding recruitment, and ensuring patient safety and scientific rigor. Howeve...
BACKGROUND: Overscanning is a common issue in CT planning, leading to unnecessary radiation exposure. PURPOSE: To develop a deep learning model to seg...
Biometric recognition based on electroencephalography (EEG), which captures intrinsic neural dynamics via scalp-recorded electrical activity, has show...
BACKGROUND: Effective communication about clinical trials is essential, as low enrollment undermines scientific validity and contributes to health car...
IMPORTANCE: Early detection of risk of heart failure with reduced ejection fraction remains challenging in resource-limited settings due to limited ac...
OBJECTIVES: To develop a random survival forest (RSF) machine learning (ML) model for predicting venous thromboembolism (VTE) risk in rheumatoid arthr...
Chronic low back pain (CLBP) is prevalent. Understanding its progression and identifying related predictors is essential to guide its management. This...
Gender-affirming surgery (GAS) and gender-affirming hormone therapy (GAHT) and are evidence-based components of care that support the health and well-...
IMPORTANCE: Mortality among children and young adolescents receiving antiretroviral therapy (ART) remains substantially higher in resource-limited set...