@inproceedings{8b8cb4e56ba740ff833b061ee16a43e1,
title = "Augmentation of electronic medical record data for deep learning",
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.",
keywords = "Deep Learning, Electronic Health Records",
author = "Georgina Kennedy and Mark Dras and Blanca Gallego",
note = "Copyright{\textcopyright} 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.; 18th World Congress on Medical and Health Informatics: One World, One Health - Global Partnership for Digital Innovation, MEDINFO 2021 ; Conference date: 02-10-2021 Through 04-10-2021",
year = "2022",
month = jun,
day = "6",
doi = "10.3233/SHTI220144",
language = "English",
isbn = "9781643682648",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press",
pages = "582--586",
editor = "Paula Otero and Philip Scott and Martin, {Susan Z.} and Elaine Huesing",
booktitle = "MEDINFO 2021: One World, One Health - Global Partnership for Digital Innovation",
address = "Netherlands",
}