Learning hidden patterns from patient multivariate time series data using convolutional neural networks: A case study of healthcare cost prediction.

Journal: Journal of biomedical informatics
Published Date: Sep 25, 2020

Abstract

OBJECTIVE: To develop an effective and scalable individual-level patient cost prediction method by automatically learning hidden temporal patterns from multivariate time series data in patient insurance claims using a convolutional neural network (CNN) architecture.

Authors

  • Mohammad Amin Morid
    Department of Operations and Information Systems, David Eccles School of Business, University of Utah, Salt Lake City, UT, USA.
  • Olivia R Liu Sheng
    Department of Operations and Information Systems, David Eccles School of Business, University of Utah, Salt Lake City, UT, USA.
  • Kensaku Kawamoto
    Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
  • Samir Abdelrahman
    Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.

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