AI Medical Compendium Topic

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Explosion of formulaic research articles, including inappropriate study designs and false discoveries, based on the NHANES US national health database.

PLoS biology
With the growth of artificial intelligence (AI)-ready datasets such as the National Health and Nutrition Examination Survey (NHANES), new opportunities for data-driven research are being created, but also generating risks of data exploitation by pape...

The Evolution of Patient Empowerment and Its Impact on Health Care's Future.

Journal of medical Internet research
In the 21st century, health care has been going through a paradigm shift called digital health. Due to major advances and breakthroughs in information technologies, most recently artificial intelligence, the patriarchy of the doctor-patient relations...

Association between urinary cadmium levels and increased gallstone disease in US adults.

Scientific reports
Heavy metal exposure is acknowledged as a risk factor for poor health. However, the effect of heavy metal exposure on the prevalence of gallstones is still unknown. Therefore, we investigated the relationship between heavy metal concentrations and th...

What drives the effectiveness of social distancing in combating COVID-19 across U.S. states?

PloS one
We propose a new theory of information-based voluntary social distancing in which people's responses to disease prevalence depend on the credibility of reported cases and fatalities and vary locally. We embed this theory into a new pandemic predictio...

Comparison of dynamic mode decomposition with other data-driven models for lung cancer incidence rate prediction.

Frontiers in public health
INTRODUCTION: Public health data analysis is critical to understanding disease trends. Existing analysis methods struggle with the complexity of public health data, which includes both location and time factors. Machine learning offers powerful tools...

Identifying high-dose opioid prescription risks using machine learning: A focus on sociodemographic characteristics.

Journal of opioid management
OBJECTIVE: The objective of this study was to leverage machine learning techniques to analyze administrative claims and socioeconomic data, with the aim of identifying and interpreting the risk factors associated with high-dose opioid prescribing.

Dual-stream algorithms for dementia detection: Harnessing structured and unstructured electronic health record data, a novel approach to prevalence estimation.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Identifying individuals with dementia is crucial for prevalence estimation and service planning, but reliable, scalable methods are lacking. We developed novel set algorithms using both structured and unstructured electronic health reco...

Association Between Risk Factors and Major Cancers: Explainable Machine Learning Approach.

JMIR cancer
BACKGROUND: Cancer is a life-threatening disease and a leading cause of death worldwide, with an estimated 611,000 deaths and over 2 million new cases in the United States in 2024. The rising incidence of major cancers, including among younger indivi...

Linguistic Markers of Pain Communication on X (Formerly Twitter) in US States With High and Low Opioid Mortality: Machine Learning and Semantic Network Analysis.

Journal of medical Internet research
BACKGROUND: The opioid epidemic in the United States remains a major public health concern, with opioid-related deaths increasing more than 8-fold since 1999. Chronic pain, affecting 1 in 5 US adults, is a key contributor to opioid use and misuse. Wh...

Heterogeneity of diagnosis and documentation of post-COVID conditions in primary care: A machine learning analysis.

PloS one
BACKGROUND: Post-COVID conditions (PCC) have proven difficult to diagnose. In this retrospective observational study, we aimed to characterize the level of variation in PCC diagnoses observed across clinicians from a number of methodological angles a...