Machine Learning–Based Models Incorporating Social Determinants of Health vs Traditional Models for Predicting In-Hospital Mortality in Patients With Heart Failure

Author:

Segar Matthew W.1,Hall Jennifer L.2,Jhund Pardeep S.3,Powell-Wiley Tiffany M.45,Morris Alanna A.6,Kao David7,Fonarow Gregg C.89,Hernandez Rosalba10,Ibrahim Nasrien E.11,Rutan Christine12,Navar Ann Marie1314,Stevens Laura M.27,Pandey Ambarish13

Affiliation:

1. Department of Cardiology, Texas Heart Institute, Houston

2. Data Science, American Heart Association, Dallas, Texas

3. British Heart Foundation Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, Scotland

4. Social Determinants of Obesity and Cardiovascular Risk Laboratory, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland

5. Intramural Research Program, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland

6. Division of Cardiology, Emory University School of Medicine, Atlanta, Georgia

7. Divisions of Cardiology and Bioinformatics + Personalized Medicine, University of Colorado School of Medicine, Aurora

8. Ahmanson-UCLA Cardiomyopathy Center, Ronald Reagan UCLA Medical Center, Los Angeles, California

9. Associate Editor for Health Care Quality and Guidelines, JAMA Cardiology

10. School of Social Work, University of Illinois at Urbana-Champaign, Urbana

11. Heart Failure Clinical Research, Inova Heart and Vascular Institute, Washington, DC

12. Quality, Outcomes Research and Analytics, American Heart Association, Dallas, Texas

13. Department of Internal Medicine, Division of Cardiology, University of Texas Southwestern Medical Center, Dallas

14. Deputy Editor, Diversity, Equity and Inclusion, JAMA Cardiology

Publisher

American Medical Association (AMA)

Subject

Cardiology and Cardiovascular Medicine

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3