AIMC Topic: Health Status Disparities

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Value of Neighborhood Socioeconomic Status in Predicting Risk of Outcomes in Studies That Use Electronic Health Record Data.

JAMA network open Sep 7, 2018
IMPORTANCE: Data from electronic health records (EHRs) are increasingly used for risk prediction. However, EHRs do not reliably collect sociodemographic and neighborhood information, which has been shown to be associated with health. The added contri...
Social Class Health Status Disparities North Carolina Racial Groups Middle Aged Humans Aged Outcome Assessment, Health Care Cohort Studies Electronic Health Records Adult Income Residence Characteristics Female Male Social Determinants of Health
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Prevalence and Disparities in Tobacco Product Use Among American Indians/Alaska Natives - United States, 2010-2015.

MMWR. Morbidity and mortality weekly report Dec 22, 2017
An overarching goal of Healthy People 2020 is to achieve health equity, eliminate disparities, and improve health among all groups.* Although significant progress has been made in reducing overall commercial tobacco product use, disparities persist, ...
Alaska Natives Humans Female Male Middle Aged Health Status Disparities Socioeconomic Factors Prevalence Tobacco Use Disorder Indians, North American Risk Factors Tobacco Products Young Adult Adolescent Adult Health Surveys United States
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Racial/Ethnic Disparities in Rates of Traumatic Injury in Arizona, 2011-2012.

Public health reports (Washington, D.C. : 1974) Aug 22, 2016
OBJECTIVE: The purpose of this study was to compare the rates of traumatic injury among five racial/ethnic groups in Arizona and to identify which mechanisms and intents of traumatic injury were predominant in each group.
Humans Retrospective Studies Residence Characteristics Ethnicity Racial Groups Alcohol Drinking Wounds and Injuries Health Status Disparities Cause of Death Arizona Intention
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African-American Men with Low-Risk Prostate Cancer: Modern Treatment and Outcome Trends.

Journal of racial and ethnic health disparities Dec 16, 2014
OBJECTIVE: To investigate the clinical characteristics and treatment patterns for African-American (AA) men with low-risk prostate cancer (PCa) using a national, population-based dataset.
Prostatic Neoplasms White People Black or African American Health Status Disparities Humans Databases, Factual Risk Assessment Adult United States Retrospective Studies Treatment Outcome Male Aged Middle Aged
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Improving the mapping of leisure-time physical activity inequities: the use of artificial intelligence to advance estimates of small-areas in Brazil.

Public health Jun 1, 2025
OBJECTIVE: We estimated the prevalence of leisure-time physical activity (LTPA) in small areas of the city of Belo Horizonte and analyzed inequities across areas and between two time periods, 2009-2013 and 2014-2018.
Artificial Intelligence Female Adult Exercise Humans Leisure Activities Male Socioeconomic Factors Middle Aged Health Status Disparities Brazil
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Bias at warp speed: how AI may contribute to the disparities gap in the time of COVID-19.

Journal of the American Medical Informatics Association : JAMIA Jan 15, 2021
The COVID-19 pandemic is presenting a disproportionate impact on minorities in terms of infection rate, hospitalizations, and mortality. Many believe artificial intelligence (AI) is a solution to guide clinical decision-making for this novel disease,...
Health Status Disparities Resource Allocation Clinical Decision-Making Healthcare Disparities Humans United States Minority Groups Artificial Intelligence Bias Information Storage and Retrieval COVID-19
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Addressing health disparities in the Food and Drug Administration's artificial intelligence and machine learning regulatory framework.

Journal of the American Medical Informatics Association : JAMIA Dec 9, 2020
The exponential growth of health data from devices, health applications, and electronic health records coupled with the development of data analysis tools such as machine learning offer opportunities to leverage these data to mitigate health disparit...
Software United States Humans United States Food and Drug Administration Artificial Intelligence Minority Groups Machine Learning Health Status Disparities Government Regulation
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Teaching yourself about structural racism will improve your machine learning.

Biostatistics (Oxford, England) Apr 1, 2020
In this commentary, we put forth the following argument: Anyone conducting machine learning in a health-related domain should educate themselves about structural racism. We argue that structural racism is a critical body of knowledge needed for gener...
Health Services Research Humans Biostatistics Machine Learning Racism Biomedical Research Healthcare Disparities Health Status Disparities
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