Medicare

Transforming oncology clinical trial matching through neuro-symbolic, multi-agent AI and an oncology-specific knowledge graph: a prospective evaluation in 3804 patients.

BACKGROUND: Clinical trial enrollment in oncology remains critically low, with fewer than 5% of eligible adults participating, in large part due to the complexity and labor intensity of eligibility screening. We prospectively evaluated a neuro-symbolic, multi-agent artificial intelligence (AI) platform integrating domain-specific large language model (LLM) agents, an oncology-specific knowledge gr...

Preparing Physicians for Tomorrow's Practice: Contemporary Practice Competencies as a Framework for Technology-Driven Change.

Medical education has long relied on stable, high-level program objectives to articulate the outcomes of undergraduate medical training. These objectives have served an essential role in defining professional identity, guiding curricular design, and ensuring accountability. However, the pace of contemporary clinical change increasingly exceeds the capacity of static curricular structures to adapt....

Deep learning reconstruction for 40-keV virtual monoenergetic CT of colon cancer: evaluation of image quality and edge sharpness.

BACKGROUND: Virtual monoenergetic imaging (VMI) at 40 keV improves iodine attenuation in colon cancer CT but is constrained by severe image noise. Dee...

Tokenizing loops of antibodies.

The complementarity-determining regions (CDRs) of antibodies are loop structures that are key to their interactions with antigens and are of high impo...

Transforming Health Care AI Education Through Micro-learning: A Novel Partnership Model for Nursing Workforce Development.

Health care professionals face an urgent need for AI literacy as artificial intelligence technologies rapidly transform clinical practice, yet nursing...

Deterioration of immunization resilience: A Bayesian machine learning assessment of the 2025-2026 measles outbreak in Mexico.

OBJECTIVE: The 2025 measles outbreak in Mexico (5741 cases) marked a severe decline in immunization resilience. We aimed to measure this systemic vuln...

Artificial Intelligence-Based Exosome Analysis for Improving Diagnostic Performance of Breast Lesions on Ultrasound: Protocol of a Prospective, Multicenter Cohort Study.

PURPOSE: Exosome-surface enhanced Raman spectroscopy-artificial intelligence platform (exosome-SERS-AI) is an innovative liquid biopsy method that acq...

Factors Influencing Universal Coverage of AI-Assisted Cervical Cancer Screening: Qualitative Study Based on the Macro Model of Health System.

BACKGROUND: Improving screening coverage is a central goal of the global strategy to eliminate cervical cancer. In resource-constrained settings, insu...

Development and Validation of Machine Learning Models for Predicting Early Cognitive Decline Using Home Sensor-Derived Behavioral Data: Sensors in-Home for Elder Wellbeing (SINEW) Cohort Study.

BACKGROUND: As the global population continues to age, the prevalence of geriatric conditions, including dementia and frailty, is also increasing. Ear...

A Machine Learning Model to Improve Risk Adjustment Accuracy in Medicare.

OBJECTIVE: To develop a machine learning (ML) algorithm that improves accuracy compared to the Hierarchical Condition Category (HCC) score used by the...

Game Changers in the Treatment of and Engagement With Persons Living With Mental Health and Substance Use Disorders: The Role of the Nurse.

The game changers in mental health and substance use disorder treatment have been shaped by historical sea changes marked by transformative advancemen...

Spatiotemporal characteristics and driving factors of coal-burning hotspots in Shandong Province revealed by multi-source data and machine learning.

Coal combustion emissions significantly contribute to air pollution in China, especially in the residential sector, where they are widely dispersed an...

Smartphone-based digital phenotyping for detection of high-risk depression and anxiety in Korean community settings.

BACKGROUND: Smartphones generate continuous behavioral signals such as mobility and activity patterns, offering scalable opportunities for monitoring ...

Advanced machine learning approaches for predicting neglected tropical disease co-endemicity in Kenya: A focus on soil-transmitted helminths, schistosomiasis, and lymphatic filariasis.

BACKGROUND: Neglected Tropical Diseases (NTDs) affect 1.5 billion people worldwide with 39% of the global burden occurring in Africa. In Kenya, NTDs r...

Introduction to secure data sharing in primary care using the federated causal learning models.

OBJECTIVES: In primary healthcare research, there are core challenges such as data silos and missing data. Furthermore, the current high technical bar...

Uncertainty aware and explainable construction cost prediction using a hybrid probabilistic learning model.

This paper presents a unified probabilistic framework for construction cost forecasting, NGBoost-ETR (Natural Gradient Boosting with Extra Trees base ...

Identifying systemic lupus erythematosus from serum proteomic profiles using machine learning and genetic risk stratification.

OBJECTIVES: Proteome-wide risk models for lupus remain underexplored. We developed classification models to identify lupus from serum proteomic profil...

Large Language Model-Powered Diagnostic Co-Pilot ("CapyEngine") for Mental Disorders: Development, Evaluation, and Future Optimization Study.

BACKGROUND: Despite the growing potential of large language models (LLMs) in mental health services, evidence on its capabilities in diagnostic proces...

Differential Factors Associated With the Presence of Persistent Symptoms in Individuals Diagnosed With Long COVID: Protocol for a Longitudinal Matched Case-Control Study.

BACKGROUND: Long COVID (postacute sequelae of SARS-CoV-2 infection) is a heterogeneous condition with persistent multisystem symptoms and substantial ...

Integration of alternative fragmentation techniques into standard LC-MS workflows using a single deep learning model enhances proteome coverage.

Bottom-up proteomics relies predominantly on collision-induced dissociation (CID) for peptide sequencing, which has achieved remarkable sensitivity an...