@article{8a372fb4899c42aea665e8bcc254e950,
title = "Utilizing Artificial Intelligence to Enhance Health Equity Among Patients with Heart Failure",
keywords = "Artificial intelligence, Guideline-directed therapy, Health equity, Health services research, Machine learning, Racial disparities, Risk prediction",
author = "Johnson, \{Amber E.\} and Brewer, \{La Princess C.\} and Echols, \{Melvin R.\} and Sula Mazimba and Shah, \{Rashmee U.\} and Khadijah Breathett",
note = "Funding Information: Dr K. Breathett has research funding from National Heart, Lung, and Blood Institute (NHLBI) K01HL142848 , R56HL159216, R25HL126146 subaward 11692sc, L30HL148881; and Women as One Escalator Award. Dr L.C. Brewer was supported by the American Heart Association-Amos Medical Faculty Development Program (Grant No. 19AMFDP35040005), NCATS (NCATS, CTSA Grant No. KL2 TR002379 ), the National Institutes of Health (NIH)/National Institute on Minority Health and Health Disparities (NIMHD) (Grant No. 1 R21 MD013490–01), and the Centers for Disease Control and Prevention (CDC) (Grant No. CDC-DP18-1817 ) during the implementation of this work. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of NCATS, NIH, or CDC. The funding bodies had no role in study design; in the collection, analysis, and interpretation of data; writing of the article; and in the decision to submit the article for publication. Dr R.U. Shah has research support from the Doris Duke Charitable Foundation and the Women as One Escalator Award.",
year = "2022",
month = apr,
doi = "10.1016/j.hfc.2021.11.001",
language = "English (US)",
volume = "18",
pages = "259--273",
journal = "Heart Failure Clinics",
issn = "1551-7136",
publisher = "Elsevier Inc.",
number = "2",
}