A Novel Deep Learning Pipeline to Analyze Temporal Clinical Data

T. Elizabeth Workman, Michael Hirezi, Eduardo Trujillo-Rivera, Anita K. Patel, Julia A. Heneghan, James E. Bost, Qing Zeng-Treitler, Murray Pollack

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Analysis of clinical temporal data can be difficult due to natural properties that often characterize it. The large number of variables, missing values, and other characteristics lead to issues of sparsity and high dimensional complexity. We hypothesized that a pipeline application implementing relevant deep learning methods could sequentially address these difficulties, demonstrating their combined utility in a classification task. We implemented Word2Vec, t-distributed stochastic neighbor embedding, and a convolutional neural network in a pipeline application. To test the pipeline, we applied it to a simple, binary classification task to identify patient encounter care setting. In preliminary testing, the pipeline application achieved 92% accuracy. It also produced temporal data cubes indicative of clinical encounters in intensive care unit (ICU) and non-ICU care settings. A deep learning pipeline process combining multiple methods holds promise in improving analytical tasks of clinical temporal data.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
EditorsYang Song, Bing Liu, Kisung Lee, Naoki Abe, Calton Pu, Mu Qiao, Nesreen Ahmed, Donald Kossmann, Jeffrey Saltz, Jiliang Tang, Jingrui He, Huan Liu, Xiaohua Hu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2879-2883
Number of pages5
ISBN (Electronic)9781538650356
DOIs
StatePublished - Jan 22 2019
Externally publishedYes
Event2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States
Duration: Dec 10 2018Dec 13 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018

Conference

Conference2018 IEEE International Conference on Big Data, Big Data 2018
CountryUnited States
CitySeattle
Period12/10/1812/13/18

Bibliographical note

Funding Information:
Funding for this research was provided in whole or in part by Mallinckrodt LLC. This publication was supported by Award Numbers UL1TR001876 and KL2TR001877 from the NIH National Center for Advancing Translational Sciences. XXX-X-XXXX-XXXX-X/XX/$XX.00 ©20XX IEEE

Funding Information:
We express gratitude to Mallinckrodt, LLC, and the NIH National Center for Advancing Translational Sciences, for their generous funding. This publication’s contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Center for Advancing Translational Sciences, or the National Institutes of Health, or the other affiliate organizations.

Keywords

  • biomedical data
  • data visualization
  • deep learning
  • pipeline application
  • temporal data

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