Using multivariate long short-term memory neural network to detect aberrant signals in health data for quality assurance.

Journal: International journal of medical informatics
Published Date: Dec 16, 2020

Abstract

BACKGROUND: The data quality of electronic health records (EHR) has been a topic of increasing interest to clinical and health services researchers. One indicator of possible errors in data is a large change in the frequency of observations in chronic illnesses. In this study, we built and demonstrated the utility of a stacked multivariate LSTM model to predict an acceptable range for the frequency of observations.

Authors

  • Seyed M Miran
    Biomedical Informatics Center, George Washington University, Washington, DC, 20052, U.S.A. miran@gwu.edu.
  • Stuart J Nelson
    George Washington University, Washington, DC, DC, United States.
  • Doug Redd
    IDEAS Center, Veterans Administration, Salt Lake City Health Care System, Salt Lake City, UT, United States; Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, United States.
  • Qing Zeng-Treitler
    Veterans Affairs Medical Center, Washington, DC; George Washington University, Washington, DC.

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