Optimizing hepatitis B virus screening in the United States using a simple demographics-based model.

Journal: Hepatology (Baltimore, Md.)
PMID: 34496066

Abstract

BACKGROUND AND AIMS: Chronic hepatitis B (CHB) affects >290 million persons globally, and only 10% have been diagnosed, presenting a severe gap that must be addressed. We developed logistic regression (LR) and machine learning (ML; random forest) models to accurately identify patients with HBV, using only easily obtained demographic data from a population-based data set.

Authors

  • Nathan S Ramrakhiani
    Division of Gastroenterology and HepatologyStanford University Medical CenterPalo AltoCaliforniaUSA.
  • Vincent L Chen
    Division of Gastroenterology and HepatologyUniversity of MichiganAnn ArborMichiganUSA.
  • Michael Le
    Division of Gastroenterology and HepatologyStanford University Medical CenterPalo AltoCaliforniaUSA.
  • Yee Hui Yeo
    Division of Gastroenterology and HepatologyStanford University Medical CenterPalo AltoCaliforniaUSA.
  • Scott D Barnett
    Developing Brain Institute, Division of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, District of Columbia, USA.
  • Akbar K Waljee
    VA Center for Clinical Management Research, VA Ann Arbor Health Care System, Ann Arbor, Michigan.
  • Ji Zhu
    Department of Statistics, University of Michigan, Ann Arbor, Michigan.
  • Mindie H Nguyen
    Division of Gastroenterology and HepatologyStanford University Medical CenterPalo AltoCaliforniaUSA.

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