Topic modeling to discern irregular order patterns in unlabeled electronic health records

Ozgur Ozmen; Hilda B. Klasky; Olufemi A. Omitaomu; Mohammed M. Olama; Teja Kuruganti; Laura Pullum; Merry Ward; Jeanie M. Scott; Angela Laurio; Jonathan Nebeker

doi:10.1109/BHI.2019.8834659

Topic modeling to discern irregular order patterns in unlabeled electronic health records

Ozgur Ozmen, Hilda B. Klasky, Olufemi A. Omitaomu, Mohammed M. Olama, Teja Kuruganti, Laura Pullum, Merry Ward, Jeanie M. Scott, Angela Laurio, Jonathan Nebeker

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

This paper presents an application of topic modeling on event sequences of Electronic Health Record (EHR) orders. Metaphorically, we approach clinical order event sequences of unlabeled data as if they are documents where words are the events that occurred in the history of an order. We demonstrate the approach leveraging Consult orders data. The details of the data preprocessing and the data structures are described along with the data sources. Latent Dirichlet Allocation (LDA) is leveraged to fit against the limited datasets prepared. Another open source tool - LDAvis is used for exploratory analysis of the LDA results. The preliminary results revealed some order patterns that are qualitatively evaluated as potential irregular transitions. The goal of this analysis is to provide unsupervised learning application to domain experts in the absence of labeled data where they can investigate captured patterns and identify irregular transitions of orders. Ultimately, such efforts will guide formalization of hazard detection algorithms that monitor EHR data to identify health information technology related hazards.

Original language	English
Title of host publication	2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728108483
DOIs	https://doi.org/10.1109/BHI.2019.8834659
State	Published - May 2019
Event	2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Chicago, United States Duration: May 19 2019 → May 22 2019

Publication series

Name	2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings

Conference

Conference	2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019
Country/Territory	United States
City	Chicago
Period	05/19/19 → 05/22/19

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).

Keywords

Computerized provider records systems
Electronic health records
Hazard detection
Health information technology
Latent Dirichlet Allocation
Topic modeling
Unsupervised learning

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10.1109/BHI.2019.8834659

Cite this

Ozmen, O., Klasky, H. B., Omitaomu, O. A., Olama, M. M., Kuruganti, T., Pullum, L., Ward, M., Scott, J. M., Laurio, A., & Nebeker, J. (2019). Topic modeling to discern irregular order patterns in unlabeled electronic health records. In 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings Article 8834659 (2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BHI.2019.8834659

Ozmen, Ozgur ; Klasky, Hilda B. ; Omitaomu, Olufemi A. et al. / Topic modeling to discern irregular order patterns in unlabeled electronic health records. 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. (2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings).

@inproceedings{6c4413ed523e434f9fcd6f22a784a295,

title = "Topic modeling to discern irregular order patterns in unlabeled electronic health records",

abstract = "This paper presents an application of topic modeling on event sequences of Electronic Health Record (EHR) orders. Metaphorically, we approach clinical order event sequences of unlabeled data as if they are documents where words are the events that occurred in the history of an order. We demonstrate the approach leveraging Consult orders data. The details of the data preprocessing and the data structures are described along with the data sources. Latent Dirichlet Allocation (LDA) is leveraged to fit against the limited datasets prepared. Another open source tool - LDAvis is used for exploratory analysis of the LDA results. The preliminary results revealed some order patterns that are qualitatively evaluated as potential irregular transitions. The goal of this analysis is to provide unsupervised learning application to domain experts in the absence of labeled data where they can investigate captured patterns and identify irregular transitions of orders. Ultimately, such efforts will guide formalization of hazard detection algorithms that monitor EHR data to identify health information technology related hazards.",

keywords = "Computerized provider records systems, Electronic health records, Hazard detection, Health information technology, Latent Dirichlet Allocation, Topic modeling, Unsupervised learning",

author = "Ozgur Ozmen and Klasky, {Hilda B.} and Omitaomu, {Olufemi A.} and Olama, {Mohammed M.} and Teja Kuruganti and Laura Pullum and Merry Ward and Scott, {Jeanie M.} and Angela Laurio and Jonathan Nebeker",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 ; Conference date: 19-05-2019 Through 22-05-2019",

year = "2019",

month = may,

doi = "10.1109/BHI.2019.8834659",

language = "English",

series = "2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings",

}

Ozmen, O, Klasky, HB , Omitaomu, OA , Olama, MM , Kuruganti, T, Pullum, L, Ward, M, Scott, JM, Laurio, A & Nebeker, J 2019, Topic modeling to discern irregular order patterns in unlabeled electronic health records. in 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings., 8834659, 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019, Chicago, United States, 05/19/19. https://doi.org/10.1109/BHI.2019.8834659

Topic modeling to discern irregular order patterns in unlabeled electronic health records. / Ozmen, Ozgur; Klasky, Hilda B.; Omitaomu, Olufemi A. et al.
2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. 8834659 (2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Topic modeling to discern irregular order patterns in unlabeled electronic health records

AU - Ozmen, Ozgur

AU - Klasky, Hilda B.

AU - Omitaomu, Olufemi A.

AU - Olama, Mohammed M.

AU - Kuruganti, Teja

AU - Pullum, Laura

AU - Ward, Merry

AU - Scott, Jeanie M.

AU - Laurio, Angela

AU - Nebeker, Jonathan

PY - 2019/5

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N2 - This paper presents an application of topic modeling on event sequences of Electronic Health Record (EHR) orders. Metaphorically, we approach clinical order event sequences of unlabeled data as if they are documents where words are the events that occurred in the history of an order. We demonstrate the approach leveraging Consult orders data. The details of the data preprocessing and the data structures are described along with the data sources. Latent Dirichlet Allocation (LDA) is leveraged to fit against the limited datasets prepared. Another open source tool - LDAvis is used for exploratory analysis of the LDA results. The preliminary results revealed some order patterns that are qualitatively evaluated as potential irregular transitions. The goal of this analysis is to provide unsupervised learning application to domain experts in the absence of labeled data where they can investigate captured patterns and identify irregular transitions of orders. Ultimately, such efforts will guide formalization of hazard detection algorithms that monitor EHR data to identify health information technology related hazards.

AB - This paper presents an application of topic modeling on event sequences of Electronic Health Record (EHR) orders. Metaphorically, we approach clinical order event sequences of unlabeled data as if they are documents where words are the events that occurred in the history of an order. We demonstrate the approach leveraging Consult orders data. The details of the data preprocessing and the data structures are described along with the data sources. Latent Dirichlet Allocation (LDA) is leveraged to fit against the limited datasets prepared. Another open source tool - LDAvis is used for exploratory analysis of the LDA results. The preliminary results revealed some order patterns that are qualitatively evaluated as potential irregular transitions. The goal of this analysis is to provide unsupervised learning application to domain experts in the absence of labeled data where they can investigate captured patterns and identify irregular transitions of orders. Ultimately, such efforts will guide formalization of hazard detection algorithms that monitor EHR data to identify health information technology related hazards.

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KW - Hazard detection

KW - Health information technology

KW - Latent Dirichlet Allocation

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KW - Unsupervised learning

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DO - 10.1109/BHI.2019.8834659

M3 - Conference contribution

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T3 - 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings

BT - 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019

Y2 - 19 May 2019 through 22 May 2019

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Ozmen O, Klasky HB , Omitaomu OA , Olama MM , Kuruganti T, Pullum L et al. Topic modeling to discern irregular order patterns in unlabeled electronic health records. In 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. 8834659. (2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings). doi: 10.1109/BHI.2019.8834659

Topic modeling to discern irregular order patterns in unlabeled electronic health records

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