Anomaly detection in sequential health care data using higher-order network representation

Haoran Niu, Olufemi A. Omitaomu, Qing Cao, Ozgur Ozmen, Hilda B. Klasky, Mohammed M. Olama, Laura L. Pullum, Teja Kuruganti, Merry Ward, Angela Laurio, Jean M. Scott, Frank Drews, Jonathan R. Nebeker

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

Abstract

The ever-increasing use of information technology (IT) in health care presents new challenges to patient care. Information errors arising from the use of Health IT and their implications on care delivery and patient outcomes have been widely reported. Information errors can lead to changes in clinical decisions, care processes, and care outcomes, among others. An altered care process, for example, could interrupt the sequence of care, which could lead to changes in care decisions and/or changes in care outcomes. We define interruptions in the care process as anomalies in the care sequence. In this paper, we present a new approach based on the higher-order network (HON) representation to detect anomalies in sequential health care data using electronic health records. The results show that there are higher-order dependencies in health care data; and that the use of HON representation is more effective than the first-order network representation for detecting anomalies in sequential health care data.

Original languageEnglish
Title of host publicationProceedings of the 2020 IISE Annual Conference
EditorsL. Cromarty, R. Shirwaiker, P. Wang
PublisherInstitute of Industrial and Systems Engineers, IISE
Pages813-818
Number of pages6
ISBN (Electronic)9781713827818
StatePublished - 2020
Event2020 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2020 - Virtual, Online, United States
Duration: Nov 1 2020Nov 3 2020

Publication series

NameProceedings of the 2020 IISE Annual Conference

Conference

Conference2020 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2020
Country/TerritoryUnited States
CityVirtual, Online
Period11/1/2011/3/20

Funding

This work is sponsored by the US Department of Veterans Affairs. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

FundersFunder number
DOE Public Access Plan
U.S. Department of Energy
U.S. Department of Veterans AffairsDE-AC05-00OR22725

    Keywords

    • Electronic health records
    • Higher-order dependency
    • Process improvement
    • Process monitoring
    • System reliability

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