US Health Policy

Latest AI and machine learning research in us health policy for healthcare professionals.

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Utilization Analysis and Fraud Detection in Medicare via Machine Learning

Healthcare fraud and overutilization pose significant challenges in the United States, leading to substantial financial losses and compromised patient care. Medicare, a vital federal healthcare program, is particularly susceptible to such abuses. With over 63 million Americans enrolled in Medicare and growing expenses, the need for effective fraud detection is paramount. Traditional methods relyin...

Development and External Validation of a Machine Learning Model to Predict Restriction from Spirometry

Though European Respiratory Society and American Thoracic Society (ERS/ATS) guidelines for pulmonary function test (PFT) interpretation recommend the use of the forced vital capacity (FVC) lower limit of normal (LLN) to exclude restriction, recent data suggest that the negative predictive value (NPV) of the FVC LLN is lower than has been accepted, particularly among non-Hispanic Black patients. We...

Development and validation of a multivariable Prediction Model for Pre-diabetes and Diabetes using Easily Obtainable Clinical Data

In the US, pre-diabetes and diabetes are increasing in prevalence alongside other chronic diseases. Hemoglobin A1c is the most common diagnostic test ...

Natural language processing for scalable feature engineering and ultra-high-dimensional confounding adjustment in healthcare database studies

To improve confounding control in healthcare database studies, data-driven algorithms may empirically identify and adjust for large numbers of pre-exp...

Evaluation of Machine Learning and Traditional Statistical Models to Assess the Value of Stroke Genetic Liability for Prediction of Risk of Stroke within the UK Biobank

Stroke is one of the leading causes of mortality and long-term disability in adults over 18 years of age globally and its increasing incidence has bec...

Development of a Machine Learning Model for Predicting In-Hospital Mortality and Analyzing Associated Risk Factors Using Large Patient Samples

This study endeavors to construct a machine learning model to forecast in-hospital mortality and dissect associated risk factors, utilizing a vast dat...

A simple feed forward neural network to predict the 2025 outbreak of measles in the USA

Measles is a highly contagious viral disease associated with a variety of severe complications. Since 1963, widespread usage of a highly effective vac...

AI Implementation in U.S. Healthcare and Its Association With Elder Mortality and Quality of Care

Hospitals are increasingly adopting artificial intelligence (AI) tools in clinical care. However, their overall impact on the health of older adults r...

Decoding the JAK-STAT axis in colorectal cancer with AI-HOPE-JAK-STAT: A conversational artificial intelligence approach to clinical-genomic integration

The Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway is a critical mediator of immune regulation, inflammati...

Characteristics and Early Diagnosis of Motor Neuron Disease (MND) in 67 million individuals in England: a comparative study on phenotyping models derived by AI, Knowledge Graphs and the MND Association

Motor neuron disease (MND) is a rapidly progressive and fatal neurodegenerative condition, making early diagnosis critical for optimizing patient outc...

A Systematic Review of the Application of Computational Grounded Theory Method in Healthcare Research

The integration of computational methods with traditional qualitative research approaches has emerged as a transformative paradigm in healthcare resea...

Closing the Lung Cancer Screening Gap in FQHCs with AI-Powered Clinical Decision Support

Lung cancer remains the leading cause of cancer-related mortality in the United States, with screening adherence rates below 16% nationally and even l...

Socioeconomic and Behavioral Drivers of Geographic Disparities in U.S. Cardiovascular Mortality: A Machine Learning Analysis

Substantial geographic disparities in cardiovascular disease (CVD) mortality persist across the United States. The extent to which “place” reflects un...

Improving Surrogate Endpoints for Survival Prediction Through Integration of Patient-Reported Outcomes

Overall survival (OS) remains the gold standard for oncology drug approval, but measuring it requires long follow-up and is impractical in certain onc...

Machine Learning to Investigate Life-Course Social Determinants of Loneliness among Older Adults in the US, England, Israel, and 27 European Countries during the Pandemic

Loneliness in later life is common and shaped by social determinants, with the COVID-19 pandemic and regional contexts further influencing disparities...

Oxytocin Enhances Social-Emotional Reciprocity in Autism

We evaluated whether oxytocin improves social-emotional reciprocity in children and adolescents with autism spectrum disorder (ASD) by conducting a se...

Evaluating the acceptability, usability and clinical appropriateness of Your Path, an AI-powered tool facilitating relevant access to HIV services post-HIV self-testing in South Africa

Timely linkage to HIV prevention and treatment services following HIV self-testing (HIVST) remains a challenge in many countries. While HIVST offers p...

Development and evaluation of a multivariate prediction model for diagnosing asthma in patients with clinically suspected asthma using capnography

Diagnosis of asthma in primary care is challenged by a multistep pathway with variable adherence leading to significant misdiagnosis, late diagnosis a...

When AI Meets the FDA: An Evaluation of Large Language Models Performance in Regulatory and Clinical Trial Data Extraction, Synthesis, and Analysis

Clinical and population decision-making relies on the systematic evaluation of extensive regulatory evidence. The FDA drug reviews provide detailed in...

Using expert-cited features to detect leg dystonia in cerebral palsy

Leg dystonia in cerebral palsy (CP) is debilitating but remains underdiagnosed. Routine clinical evaluation has only 12% accuracy for leg dystonia dia...

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