Medicare

Using Machine Learning to Predict Features Within Substance Use Disorder Treatment Service Settings That Increase the Likelihood of Positive Treatment Outcomes.

Given the conceptual issues involved in defining and measuring recovery and accordingly substance use disorder (SUD) treatment outcomes, the role of each state's treatment system and social factors, the objective is to examine underlying and interrelated patterns within SUD treatment, outcomes, and recovery. Using a recovery-oriented framework, a Machine Learning Random Forest model was developed ...

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 coverage effects remains computationally challenging. We present a machine learning-accelerated kinetic Monte Carlo (ML-kMC) framework to efficiently model coverage-dependent surface reactions. Our approach integrates three key functions: an automated si...

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...

Filling of incomplete sinograms from sparse PET detector configurations using a residual U-Net.

BACKGROUND: Long axial field-of-view PET scanners are becoming increasingly available worldwide for clinical and research nuclear medicine examination...

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...

DoC-Informer: Automated Discrimination of Disorders of Consciousness under Adaptive EEG Settings.

OBJECTIVE: Disorders of consciousness (DoC) diagnosis critically depends on accurate state discrimination to guide treatment and prognosis. Current EE...

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...

Explainable multimodal deep learning models for variable-length sequences in critically ill patients.

OBJECTIVE: Deep learning models have shown strong performance in predicting clinical events in critical care using structured electronic health record...

Microplastic detection and recognition system enabled by a triboelectric nanogenerator and machine learning techniques.

The toxicity of microplastic pollutants is closely associated with their material, size, and concentration. However, current detection methods are pla...

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...