Latest AI and machine learning research in medicare for healthcare professionals.
BACKGROUND: Delays in dental care worsen oral disease and mirror broader inequities in health care access and use. OBJECTIVE: To estimate the 12-month prevalence of delayed dental care among US adults, to characterise disparities across demographic and socioeconomic groups and to evaluate the utility of machine learning models for identifying individuals at elevated risk of delay. METHODS: This st...
OBJECTIVE: To develop a machine learning model to identify adult Expanded Food and Nutrition Education Program (EFNEP) participants at high risk of attrition using preprogram data. DESIGN: Secondary data analysis. PARTICIPANTS: A total of 339,335 adults participating in EFNEP nationwide from 2013-2022. MAIN OUTCOME MEASURES: Adult EFNEP attrition, defined as a participant dropping out of EFNEP bef...
The advent of long-axial-field-of-view (LAFOV) PET/CT systems has significantly improved whole-body imaging by providing higher sensitivity and extend...
BACKGROUND: As Singapore adopts a population health approach under Healthier Singapore (Healthier SG), optimizing healthcare resources is crucial. We ...
Reported instances of AI-assisted, blanket denials of coverage have increased in recent years, particularly for Medicare Advantage plans, resulting in...
PURPOSE: Accurate prediction of beam delivery time (BDT) is critical for operational efficiency, 4D dose calculations, and advanced proton therapy tec...
BACKGROUND: Despite progress in childhood vaccination, many children in low- and middle-income countries, including Ethiopia, remain unvaccinated, pre...
BACKGROUND: Volumetric modulated arc therapy (VMAT) machine parameter optimization (MPO) is a complex, high-dimensional problem typically solved with ...
Clinical trial enrollment in oncology remains limited by increasingly complex eligibility criteria, biomarker stratification, and fragmented clinical ...
Machine-learning-based interatomic potentials are widely employed in atomistic simulations, but they struggle to capture long-range electrostatic corr...
BACKGROUND: Predicting health insurance uptake remains a critical challenge for policymakers and insurance providers seeking to optimise coverage stra...
Reference libraries of tandem mass spectra (MS/MS) are widely used for metabolite identification in untargeted metabolomics and to train machine-learn...
BACKGROUND: Cognitive fatigue is a frequently reported and debilitating symptom of long COVID, yet effective therapeutic interventions remain limited....
BACKGROUND: Delayed admission to the intensive care unit (ICU) after trauma can lead to tripling of in-hospital mortality. Accurate ICU resource predi...
Self-organization through noisy interactions is ubiquitous across physics, mathematics, and machine learning, yet how long-range structure emerges fro...
BACKGROUND: Patient recruitment for clinical trials remains a major challenge, with 86% of trials failing to meet enrollment targets on time. In over ...
BACKGROUND: Early Parkinson's disease (PD) presents with subtle symptoms and lacks specific diagnostic methods. Clinical diagnosis primarily relies on...
ETHNOPHARMACOLOGICAL RELEVANCE: Despite the fact that herbal medicine has been used for a long time, their clinical application is challenged by uncle...
BACKGROUND: Traditional patient education often lacks personalization and engagement, potentially limiting knowledge acquisition and treatment adheren...
Accurately capturing long-range interactions is critical for molecular dynamics simulations based on machine learning interatomic potentials. We recen...