Machine learning models to predict disease progression among veterans with hepatitis C virus.

Journal: PloS one
PMID: 30608929

Abstract

BACKGROUND: Machine learning (ML) algorithms provide effective ways to build prediction models using longitudinal information given their capacity to incorporate numerous predictor variables without compromising the accuracy of the risk prediction. Clinical risk prediction models in chronic hepatitis C virus (CHC) can be challenging due to non-linear nature of disease progression. We developed and compared two ML algorithms to predict cirrhosis development in a large CHC-infected cohort using longitudinal data.

Authors

  • Monica A Konerman
    Michigan Medicine, Department of Internal Medicine, Division of Gastroenterology and Hepatology, Ann Arbor, Michigan, United States of America.
  • Lauren A Beste
    Department of Medicine, Veterans Affairs Puget Sound Healthcare System and University of Washington, Seattle, WA, United States of America.
  • Tony Van
    VA Ann Arbor Health Services Research and Development Center of Clinical Management Research, Ann Arbor, Michigan, United States of America.
  • Boang Liu
    Department of Statistics, University of Michigan, Ann Arbor, Michigan.
  • Xuefei Zhang
    Department of Statistics, University of Michigan, Ann Arbor, MI, United States of America.
  • Ji Zhu
    Department of Statistics, University of Michigan, Ann Arbor, Michigan.
  • Sameer D Saini
    VA Ann Arbor HSR&D Center for Clinical Management Research.
  • Grace L Su
    Michigan Medicine, Department of Internal Medicine, Division of Gastroenterology and Hepatology, Ann Arbor, Michigan, United States of America.
  • Brahmajee K Nallamothu
    From the Division of Cardiology, Department of Medicine; Cardiovascular Research Institute; Institute for Human Genetics; and Institute for Computational Health Sciences, University of California San Francisco, and California Institute for Quantitative Biosciences (R.C.D.); and VA Health Services Research and Development Center for Clinical Management Research, VA Ann Arbor Healthcare System, MI; Michigan Center for Health Analytics and Medical Prediction (M-CHAMP), Department of Internal Medicine, University of Michigan Medical School, Ann Arbor (B.K.N.).
  • George N Ioannou
    Division of Gastroenterology, Department of Medicine, University of Washington, Seattle, WA, United States of America.
  • Akbar K Waljee
    VA Center for Clinical Management Research, VA Ann Arbor Health Care System, Ann Arbor, Michigan.

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