EMR coding with semi-parametric multi-head matching networks

Anthony Rios, Ramakanth Kavuluru

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

Abstract

Coding EMRs with diagnosis and procedure codes is an indispensable task for billing, secondary data analyses, and monitoring health trends. Both speed and accuracy of coding are critical. While coding errors could lead to more patient-side financial burden and misinterpretation of a patient's well-being, timely coding is also needed to avoid backlogs and additional costs for the healthcare facility. In this paper, we present a new neural network architecture that combines ideas from few-shot learning matching networks, multi-label loss functions, and convolutional neural networks for text classification to significantly outperform other state-of-The-Art models. Our evaluations are conducted using a well known deidentified EMR dataset (MIMIC) with a variety of multi-label performance measures.

Original languageEnglish
Title of host publicationLong Papers
Pages2081-2091
Number of pages11
ISBN (Electronic)9781948087278
StatePublished - 2018
Event2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018 - New Orleans, United States
Duration: Jun 1 2018Jun 6 2018

Publication series

NameNAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference
Volume1

Conference

Conference2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018
Country/TerritoryUnited States
CityNew Orleans
Period6/1/186/6/18

Bibliographical note

Funding Information:
Thanks to anonymous reviewers for their thorough reviews and constructive criticism that helped improve the clarity of the paper (especially leading to the addition of Section 3.5 in the revision). This research is supported by the U.S. National Library of Medicine through grant R21LM012274. We also gratefully acknowledge the support of the NVIDIA Corporation for its donation of the Titan X Pascal GPU used for this research.

Publisher Copyright:
© 2018 The Association for Computational Linguistics.

ASJC Scopus subject areas

  • Linguistics and Language
  • Language and Linguistics
  • Computer Science Applications

Fingerprint

Dive into the research topics of 'EMR coding with semi-parametric multi-head matching networks'. Together they form a unique fingerprint.

Cite this