Categorization of free-text drug orders using character-level recurrent neural networks.

Journal: International journal of medical informatics
Published Date: May 23, 2019

Abstract

BACKGROUND AND PURPOSE: Manual annotation and categorization of non-standardized text ("free-text") of drug orders entered into electronic health records is a labor-intensive task. However, standardization is required for drug order analyses and has implications for clinical decision support. Machine learning could help to speed up manual labelling efforts. The objective of this study was to analyze the performance of deep machine learning methods to annotate non-standardized text of drug order entries with their therapeutically active ingredients.

Authors

  • Yarden Raiskin
    Dept. of Mathematics, Seminar for Statistics, ETH Zurich, Universitätstrasse 6, 8092, Zurich, Switzerland.
  • Carsten Eickhoff
    Department of Computer Science, ETH Zurich, Zurich, Switzerland; Center for Biomedical Informatics, Brown University, Providence, RI, USA.
  • Patrick E Beeler
    Department of Internal Medicine, University Hospital Zurich and University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland. Electronic address: patrick.beeler@usz.ch.

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