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Comparing High Dimensional Word Embeddings Trained on Medical Text to Bag-of-Words for Predicting Medical Codes

  • Vithya YogarajanEmail author
  • Henry Gouk
  • Tony Smith
  • Michael Mayo
  • Bernhard Pfahringer
Conference paper
  • 300 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12033)

Abstract

Word embeddings are a useful tool for extracting knowledge from the free-form text contained in electronic health records, but it has become commonplace to train such word embeddings on data that do not accurately reflect how language is used in a healthcare context. We use prediction of medical codes as an example application to compare the accuracy of word embeddings trained on health corpora to those trained on more general collections of text. It is shown that both an increase in embedding dimensionality and an increase in the volume of health-related training data improves prediction accuracy. We also present a comparison to the traditional bag-of-words feature representation, demonstrating that in many cases, this conceptually simple method for representing text results in superior accuracy to that of word embeddings.

Keywords

Word embeddings Binary classification Machine learning for health 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of WaikatoHamiltonNew Zealand
  2. 2.School of InformaticsUniversity of EdinburghEdinburghScotland

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