Evaluating Social Determinants of Health Variables in Advanced Analytic and Artificial Intelligence Models for Cardiovascular Disease Risk and Outcomes: A Targeted Review.

Journal: Ethnicity & disease
Published Date: Mar 31, 2023

Abstract

INTRODUCTION/PURPOSE: Predictive models incorporating relevant clinical and social features can provide meaningful insights into complex interrelated mechanisms of cardiovascular disease (CVD) risk and progression and the influence of environmental exposures on adverse outcomes. The purpose of this targeted review (2018-2019) was to examine the extent to which present-day advanced analytics, artificial intelligence, and machine learning models include relevant variables to address potential biases that inform care, treatment, resource allocation, and management of patients with CVD.

Authors

  • Jane L Snowdon
    IBM Watson Health, Cambridge, Massachusetts, USA.
  • Elisabeth L Scheufele
    Center for Artificial Intelligence, Research, and Evaluation, IBM Watson Health, Cambridge, MA 02142.
  • Jill Pritts
    Center for Artificial Intelligence, Research, and Evaluation, IBM Watson Health, Cambridge, MA 02142.
  • Phuong-Tu Le
    Division of Integrative Biological and Behavioral Sciences, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD 20892.
  • George A Mensah
    Center for Translation Research and Implementation Science, National Heart, Lung, and Blood Institute (NHLBI), National Institutes of Health (NIH), Bethesda, Maryland, USA. George.Mensah@nih.gov.
  • Xinzhi Zhang
    Center for Translation Research and Implementation Science, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892.
  • Irene Dankwa-Mullan
    1 IBM Corporation, Watson Health, Bethesda, Maryland.

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