Accounting for racial bias and social determinants of health in a model of hypertension control

Background Hypertension control remains a critical problem and most of the existing literature views it from a clinical perspective, overlooking the role of sociodemographic factors. This study aims to identify patients with not well-controlled hypertension using readily available demographic and so...

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Veröffentlicht in:	BMC medical informatics and decision making Jg. 25; H. 1; S. 53 - 12
Hauptverfasser:	Hu, Yang, Cordella, Nicholas, Mishuris, Rebecca G., Paschalidis, Ioannis Ch
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 03.02.2025 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Adult Aged Analysis Bias Bias detection and mitigation in medical informatics Black or African American - statistics & numerical data Blood pressure Boston Care and treatment Cigarettes Community support Complications and side effects Datasets Decision trees Demographic aspects Demographic variables Demographics Demography Economic aspects Electronic health records Family income Female Health care disparities Health care facilities Health disparities Health Informatics Heart failure Humans Hypertension Hypertension - ethnology Hypertension - therapy Information Systems and Communication Service Machine learning Male Management of Computing and Information Systems Marital status Medical centers Medical diagnosis Medical research Medicine Medicine & Public Health Medicine, Experimental Mental depression Mental health Methods Middle Aged Patients Postal codes Prediction models Predictive control Prevention Prognosis Questionnaires Race Racial bias Racism Racism - statistics & numerical data Retrospective Studies Social aspects Social determinants of health Social Determinants of Health - ethnology Social Determinants of Health - statistics & numerical data Social factors Sociodemographics Socioeconomic factors Socioeconomics Variables White Boston United States United States > US Hypertension Social determinants of health Racial bias Machine learning
ISSN:	1472-6947, 1472-6947
Online-Zugang:	Volltext
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