Augmentation of electronic medical record data for deep learning

Georgina Kennedy*, Mark Dras, Blanca Gallego

*Corresponding author for this work

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

1 Citation (Scopus)
35 Downloads (Pure)

Abstract

Data imbalance is a well-known challenge in the development of machine learning models. This is particularly relevant when the minority class is the class of interest, which is frequently the case in models that predict mortality, specific diagnoses or other important clinical end-points. Typical methods of dealing with this include over-or under-sampling training data, or weighting the loss function in order to boost the signal from the minority class. Data augmentation is another frequently employed method-particularly for models that use images as input data. For discrete time-series data, however, there is no consensus method of data augmentation. We propose a simple data augmentation strategy that can be applied to discrete time-series data from the EMR. This strategy is then demonstrated using a publicly available data-set, in order to provide proof of concept for the work undertaken in [1], where data is unable to be made open.

Original languageEnglish
Title of host publicationMEDINFO 2021: One World, One Health - Global Partnership for Digital Innovation
Subtitle of host publicationProceedings of the 18th World Congress on Medical and Health Informatics
EditorsPaula Otero, Philip Scott, Susan Z. Martin, Elaine Huesing
Place of PublicationAmsterdam
PublisherIOS Press
Pages582-586
Number of pages5
ISBN (Electronic)9781643682655
ISBN (Print)9781643682648
DOIs
Publication statusPublished - 6 Jun 2022
Event18th World Congress on Medical and Health Informatics: One World, One Health - Global Partnership for Digital Innovation, MEDINFO 2021 - Virtual, Online
Duration: 2 Oct 20214 Oct 2021

Publication series

NameStudies in Health Technology and Informatics
Volume290
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Conference

Conference18th World Congress on Medical and Health Informatics: One World, One Health - Global Partnership for Digital Innovation, MEDINFO 2021
CityVirtual, Online
Period2/10/214/10/21

Bibliographical note

Copyright© 2022 International Medical Informatics Association (IMIA) and IOS Press. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • Deep Learning
  • Electronic Health Records

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