Medicare

Applying machine learning to predict stunting in children under 5 years old based on water, sanitation and hygiene behaviors and infrastructure.

OBJECTIVE: Child stunting continues to pose a substantial global health challenge, requiring multifaceted strategies that combine conventional epidemiological approaches with advanced analytic methods. The aim of this study was to determine the most effective machine learning model for predicting stunting based on water, sanitation, and hygiene behaviors and infrastructure, with the goal of identi...

Evaluation of Otolaryngology Provider Bias in Portal Messaging.

BACKGROUND/OBJECTIVES: Patient messaging portals are widely used in clinical practice and are linked to improved patient outcomes, but they are also associated with provider burnout, a phenomenon that is understood to exacerbate implicit bias. This study evaluates provider bias in responses to patient portal messages within an academic otolaryngology practice. METHODS: A total of 167,866 patient m...

A Hybrid Physics-Driven Neural Network Force Field for Liquid Electrolytes.

Electrolyte design plays an important role in the development of lithium-ion batteries and sodium-ion batteries. Battery electrolytes feature a large ...

Machine Learning-Accelerated Kinetic Simulations of Surface Reactions with Complex Coverage Effects.

The presence of multiple adsorbates and their lateral interactions significantly influence catalytic performance, yet accurately simulating these cove...

Hoof lesion detection in lactating Holsteins: Part II. Development and on-farm validation of a predictive classification model using autonomous camera system data compared to passive surveillance.

Hoof lesion detection remains a challenge in lameness management on dairy farms. Recent studies have proposed locomotion score (LS)-based thresholds u...

Integration of Short- and Long-Read RNA Sequencing Enables the Discovery of Circular RNAs.

UNLABELLED: Circular RNAs (circRNA) are associated with crucial hallmarks of tumorigenesis. Select circRNAs contain circular open reading frames (cORF...

From data to treatment plan: An AI-driven path for automated breast radiotherapy planning.

BACKGROUND: Breast cancer is one of the most prevalent malignancies in women, with radiotherapy (RT) playing a key role in its treatment. Advances in ...

Evaluation of the Effectiveness of an AI-assisted, Dual-Template Workflow in Improving the Accuracy of Tooth Autotransplantation.

OBJECTIVE: To test whether an AI-assisted, dual-template workflow improves plan-delivery accuracy in tooth autotransplantation versus a replica-only f...

Dysphagia optimized knowledge-based planning for head and neck cancer.

PURPOSE: Swallowing dysfunction after radiotherapy (RT) is often linked to pharyngeal mucosal damage. This study aimed to develop a dysphagia-optimize...

Population-specific validation of long bone linear discriminant analysis against morphoscopic pelvic methods for sex estimation in contemporary Italian skeletal remains.

Sex estimation represents a fundamental step in forensic identification protocols, traditionally relying on morphoscopic pelvic assessment. However, t...

Machine learning for site risk prediction in clinical trials: development, external validation, and operational application in site qualification.

BACKGROUND: Site selection and qualification represent critical operational challenges in clinical trials, particularly in rare diseases like transthy...

Evaluating Guideline-Adherent Antibiotic Use for Skin Infections Using Natural Language Processing: A Pilot Study.

OBJECTIVE: To develop and evaluate an internally validated natural language processing (NLP) model to determine guideline adherence of antibiotic deci...

Distinguishing post-COVID from long-COVID in adults: Development and validation of a biomarker signature using targeted proteomics and machine learning in a cross-sectional observational study.

BACKGROUND: COVID-19 can have diverse clinical manifestations, ranging from asymptomatic infection to critical illness with multiorgan involvement. Wh...

Exploring key risk factors for loss to follow-up after hospitalization for acute stroke.

BACKGROUND: A significant proportion of stroke patients are lost to follow-up (LTFU) after discharge, which may increase risks of morbidity, mortality...

Vision transformer model-based dose prediction and beam angle optimization for BNCT.

OBJECTIVE: A good BNCT treatment plan, which can deliver higher tumor dose and lower doses to organs at risk, critically depends on the accuracy of do...

AI-Driven Patient Screening for Clinical Trials in Pancreatic Cancer: The PANCR-AI Pilot Retrospective Comparative Study.

BACKGROUND: Screening for clinical trials is challenging for clinicians due to its time-consuming and repetitive nature. The rise of artificial intell...

Fuzzing the brain: Automated stress testing for the safety of ML-driven neurostimulation.

OBJECTIVE: Machine learning (ML) models are increasingly used to generate electrical stimulation patterns in neuroprosthetic devices such as visual pr...

Rapid response teams for new leaders: a narrative review of global evidence and implementation strategies with a focus on Japan.

BACKGROUND: Rapid response systems (RRSs) are designed to detect and treat physiological deterioration before cardiac arrest occurs. Since 2020, Japan...

The Trial Bank initiative to promote trustworthy living evidence for medication safety (PROMISE) of children: design and pilot validation.

BACKGROUND: While medications are essential for preventing and treating disease, they can also cause harm. Evidence synthesis has been widely adopted ...

Interpretable Ensemble Machine Learning Prediction of Nonadherence and the Risk of Nonpersistence of Targeted Disease-Modifying Antirheumatic Agents in Older Adults With Rheumatoid Arthritis.

PURPOSE: Ensemble machine learning (ML) demonstrated potential for improving predictions based on big health care data. We developed and validated int...