AIMC Topic: United States

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An interpretable machine learning model with demographic variables and dietary patterns for ASCVD identification: from U.S. NHANES 1999-2018.

BMC medical informatics and decision making
Current research on the association between demographic variables and dietary patterns with atherosclerotic cardiovascular disease (ASCVD) is limited in breadth and depth. This study aimed to construct a machine learning (ML) algorithm that can accur...

Artificial Intelligence to Enhance Precision Medicine in Cardio-Oncology: A Scientific Statement From the American Heart Association.

Circulation. Genomic and precision medicine
Artificial intelligence is poised to transform cardio-oncology by enabling personalized care for patients with cancer, who are at a heightened risk of cardiovascular disease due to both the disease and its treatments. The rising prevalence of cancer ...

Machine Learning-Based Prediction of Substance Use in Adolescents in Three Independent Worldwide Cohorts: Algorithm Development and Validation Study.

Journal of medical Internet research
BACKGROUND: To address gaps in global understanding of cultural and social variations, this study used a high-performance machine learning (ML) model to predict adolescent substance use across three national datasets.

American academy of Orthopedic Surgeons' OrthoInfo provides more readable information regarding meniscus injury than ChatGPT-4 while information accuracy is comparable.

Journal of ISAKOS : joint disorders & orthopaedic sports medicine
INTRODUCTION: Over 61% of Americans seek health information online, often using artificial intelligence (AI) tools like ChatGPT. However, concerns persist about the readability and accessibility of AI-generated content, especially for individuals wit...

Identifying major depressive disorder among US adults living alone using stacked ensemble machine learning algorithms.

Frontiers in public health
BACKGROUND: It has been increasingly recognized that adults living alone have a higher likelihood of developing Major Depressive Disorder (MDD) than those living with others. However, there is still no prediction model for MDD specifically designed f...

AI-Driven Analysis of Drug Marketing Efficiency: Unveiling FDA Approval to Market Release Dynamics.

The AAPS journal
This paper explores a novel approach using generative AI to enhance drug marketing strategies in the US pharmaceutical sector. By leveraging an official dataset sourced from the US government, the AI generates Python code to analyze the time interval...

Predicting diabetes self-management education engagement: machine learning algorithms and models.

BMJ open diabetes research & care
INTRODUCTION: Diabetes self-management education (DSME) is endorsed by the American Diabetes Association (ADA) as an essential component of diabetes management. However, the utilization of DSME remains limited in the USA. This study aimed to investig...

Comparative ranking of marginal confounding impact of natural language processing-derived versus structured features in pharmacoepidemiology.

Computers in biology and medicine
OBJECTIVE: To explore the ability of natural language processing (NLP) methods to identify confounder information beyond what can be identified using claims codes alone for pharmacoepidemiology.

Social Determinants and Health Equity Activities: Are They Connected with the Adaptation of AI and Telehealth Services in the U.S. Hospitals?

International journal of environmental research and public health
In recent decades, technological shifts within the healthcare sector have significantly transformed healthcare management and utilization, introducing unprecedented possibilities that elevate quality of life. Organizational factors are recognized as ...