A knowledge network-based approach to facilitate annotation of clinical pathway component clusters

S. M. Shamimul Hasan; Minsu Kim; Byung H. Park; Makoto M. Jones; Merry Ward; Jonathan Nebeker

doi:10.1109/BHI50953.2021.9508508

A knowledge network-based approach to facilitate annotation of clinical pathway component clusters

S. M. Shamimul Hasan, Minsu Kim, Byung H. Park, Makoto M. Jones, Merry Ward, Jonathan Nebeker

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

2 Scopus citations

Abstract

Mining electronic health records (EHRs) to identify contextually related clinical concept clusters that tend to co-occur temporarily and consistently could improve data-driven clinical pathway (CP) construction. However, the automatic extraction of contextually related clinical concept clusters contains a vast amount of irrelevant information. Hence, this paper proposes a knowledge network-enabled literature-based discovery (LBD) approach to remove noise from clusters. The authors used published literature to filter spurious concepts from the clusters and used data from the US Department of Veterans Affairs's major depressive disorder (MDD) cohort of Operation Enduring Freedom/Operation Iraqi Freedom (OEF/OIF) for their experimentation. The approach was applied to 2,967 clusters extracted from the MDD OEF/OIF database. The experimental results demonstrate that the proposed approach can filter 94% of the irrelevant information. Moreover, the authors applied various network mining algorithms to analyze the clusters and demonstrated that LBD, along with network mining techniques, is a useful method for finding accurate contextually related clinical concept clusters. This could help domain researchers perform advanced analytics in CPs.

Original language	English
Title of host publication	BHI 2021 - 2021 IEEE EMBS International Conference on Biomedical and Health Informatics, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665403580
DOIs	https://doi.org/10.1109/BHI50953.2021.9508508
State	Published - 2021
Event	2021 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2021 - Virtual, Online, Greece Duration: Jul 27 2021 → Jul 30 2021

Publication series

Name	BHI 2021 - 2021 IEEE EMBS International Conference on Biomedical and Health Informatics, Proceedings

Conference

Conference	2021 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2021
Country/Territory	Greece
City	Virtual, Online
Period	07/27/21 → 07/30/21

Funding

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

Clinical pathway
Knowledge network
Major depressive disorder
Semantic MEDLINE
Unified medical language system

Access to Document

10.1109/BHI50953.2021.9508508

Cite this

Shamimul Hasan, S. M., Kim, M., Park, B. H., Jones, M. M., Ward, M., & Nebeker, J. (2021). A knowledge network-based approach to facilitate annotation of clinical pathway component clusters. In BHI 2021 - 2021 IEEE EMBS International Conference on Biomedical and Health Informatics, Proceedings (BHI 2021 - 2021 IEEE EMBS International Conference on Biomedical and Health Informatics, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BHI50953.2021.9508508

Shamimul Hasan, S. M. ; Kim, Minsu ; Park, Byung H. et al. / A knowledge network-based approach to facilitate annotation of clinical pathway component clusters. BHI 2021 - 2021 IEEE EMBS International Conference on Biomedical and Health Informatics, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. (BHI 2021 - 2021 IEEE EMBS International Conference on Biomedical and Health Informatics, Proceedings).

@inproceedings{aeddbdad412f42ae8d832fd057f057eb,

title = "A knowledge network-based approach to facilitate annotation of clinical pathway component clusters",

abstract = "Mining electronic health records (EHRs) to identify contextually related clinical concept clusters that tend to co-occur temporarily and consistently could improve data-driven clinical pathway (CP) construction. However, the automatic extraction of contextually related clinical concept clusters contains a vast amount of irrelevant information. Hence, this paper proposes a knowledge network-enabled literature-based discovery (LBD) approach to remove noise from clusters. The authors used published literature to filter spurious concepts from the clusters and used data from the US Department of Veterans Affairs's major depressive disorder (MDD) cohort of Operation Enduring Freedom/Operation Iraqi Freedom (OEF/OIF) for their experimentation. The approach was applied to 2,967 clusters extracted from the MDD OEF/OIF database. The experimental results demonstrate that the proposed approach can filter 94% of the irrelevant information. Moreover, the authors applied various network mining algorithms to analyze the clusters and demonstrated that LBD, along with network mining techniques, is a useful method for finding accurate contextually related clinical concept clusters. This could help domain researchers perform advanced analytics in CPs.",

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author = "{Shamimul Hasan}, {S. M.} and Minsu Kim and Park, {Byung H.} and Jones, {Makoto M.} and Merry Ward and Jonathan Nebeker",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 2021 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2021 ; Conference date: 27-07-2021 Through 30-07-2021",

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Shamimul Hasan, SM, Kim, M , Park, BH, Jones, MM, Ward, M & Nebeker, J 2021, A knowledge network-based approach to facilitate annotation of clinical pathway component clusters. in BHI 2021 - 2021 IEEE EMBS International Conference on Biomedical and Health Informatics, Proceedings. BHI 2021 - 2021 IEEE EMBS International Conference on Biomedical and Health Informatics, Proceedings, Institute of Electrical and Electronics Engineers Inc., 2021 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2021, Virtual, Online, Greece, 07/27/21. https://doi.org/10.1109/BHI50953.2021.9508508

A knowledge network-based approach to facilitate annotation of clinical pathway component clusters. / Shamimul Hasan, S. M.; Kim, Minsu ; Park, Byung H. et al.
BHI 2021 - 2021 IEEE EMBS International Conference on Biomedical and Health Informatics, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. (BHI 2021 - 2021 IEEE EMBS International Conference on Biomedical and Health Informatics, Proceedings).

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

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AU - Kim, Minsu

AU - Park, Byung H.

AU - Jones, Makoto M.

AU - Ward, Merry

AU - Nebeker, Jonathan

PY - 2021

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AB - Mining electronic health records (EHRs) to identify contextually related clinical concept clusters that tend to co-occur temporarily and consistently could improve data-driven clinical pathway (CP) construction. However, the automatic extraction of contextually related clinical concept clusters contains a vast amount of irrelevant information. Hence, this paper proposes a knowledge network-enabled literature-based discovery (LBD) approach to remove noise from clusters. The authors used published literature to filter spurious concepts from the clusters and used data from the US Department of Veterans Affairs's major depressive disorder (MDD) cohort of Operation Enduring Freedom/Operation Iraqi Freedom (OEF/OIF) for their experimentation. The approach was applied to 2,967 clusters extracted from the MDD OEF/OIF database. The experimental results demonstrate that the proposed approach can filter 94% of the irrelevant information. Moreover, the authors applied various network mining algorithms to analyze the clusters and demonstrated that LBD, along with network mining techniques, is a useful method for finding accurate contextually related clinical concept clusters. This could help domain researchers perform advanced analytics in CPs.

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Shamimul Hasan SM, Kim M , Park BH, Jones MM, Ward M, Nebeker J. A knowledge network-based approach to facilitate annotation of clinical pathway component clusters. In BHI 2021 - 2021 IEEE EMBS International Conference on Biomedical and Health Informatics, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2021. (BHI 2021 - 2021 IEEE EMBS International Conference on Biomedical and Health Informatics, Proceedings). doi: 10.1109/BHI50953.2021.9508508

A knowledge network-based approach to facilitate annotation of clinical pathway component clusters

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