Machine learning for patient risk stratification for acute respiratory distress syndrome.

Journal: PloS one
Published Date: Mar 28, 2019

Abstract

BACKGROUND: Existing prediction models for acute respiratory distress syndrome (ARDS) require manual chart abstraction and have only fair performance-limiting their suitability for driving clinical interventions. We sought to develop a machine learning approach for the prediction of ARDS that (a) leverages electronic health record (EHR) data, (b) is fully automated, and (c) can be applied at clinically relevant time points throughout a patient's stay.

Authors

  • Daniel Zeiberg
    Computer Science and Engineering, University of Michigan, Ann Arbor, MI, United States of America.
  • Tejas Prahlad
    Computer Science and Engineering, University of Michigan, Ann Arbor, MI, 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.).
  • Theodore J Iwashyna
    Center for Clinical Management Research, VA Ann Arbor Health Care System, Ann Arbor, MI.
  • Jenna Wiens
    Computer Science and Engineering, University of Michigan, Ann Arbor.
  • Michael W Sjoding
    1 Department of Internal Medicine, and.

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