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Leveraging UMLS-driven NLP to enhance identification of influenza predictors derived from electronic medical record data

View ORCID ProfileKari A. Stephens, Margaret A. Au, View ORCID ProfileMeliha Yetisgen, View ORCID ProfileBarry Lutz, View ORCID ProfileMonica Zigman Suchsland, View ORCID ProfileMark H. Ebell, View ORCID ProfileMatthew Thompson
doi: https://doi.org/10.1101/2020.04.24.058982
Kari A. Stephens
1Department of Family Medicine, University of Washington, Seattle, WA, USA
2Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
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  • For correspondence: kstephen@uw.edu
Margaret A. Au
1Department of Family Medicine, University of Washington, Seattle, WA, USA
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Meliha Yetisgen
2Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
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Barry Lutz
3Department of Bioengineering, University of Washington, Seattle, WA, USA
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Monica Zigman Suchsland
1Department of Family Medicine, University of Washington, Seattle, WA, USA
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Mark H. Ebell
4Epidemiology and Biostatistics, College of Public Health University of Georgia, Athens, GA, USA
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Matthew Thompson
1Department of Family Medicine, University of Washington, Seattle, WA, USA
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ABSTRACT

Objective Multiple clinical prediction rules have been developed, but lack validation. This study aims to identify a set of prediction algorithms for influenza, based on electronic health record (EHR) structured data and clinical notes derived data using Unified Medical Language System (UMLS) driven natural language processing (NLP).

Materials and Methods Data were extracted from an enterprise-wide data warehouse for all patients who tested positive for influenza and were seen in ambulatory care between 2009 and 2019 (N = 7,278). A text processing pipeline was used to analyze chart notes for UMLS terms for symptoms of interest to improve data quality completeness. Three models, which step up complexity of the dataset and predictors, were tested with least absolute shrinkage and selection operator (LASSO)-selected parameters to identify predictors for influenza. Receiver operating characteristic (ROC) curves compared test accuracy across the three models.

Results Three models identified 7, 8, and 10 predictors, and the most complex model performed best. The addition of the UMLS-driven NLP symptoms data improved data quality (false negatives) and increased the number of significant predictors. NLP also increased the strength of the models, as did the addition of two-way predictor interactions.

Discussion The EHR is a feasible source for offering rapidly accessible datasets for influenza related prediction research that was used to produce a prediction model for influenza. Combining data collected in routine care with data science methods improved a prediction model for influenza, and in the future, could be used to drive diagnostics at the point of care.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted April 25, 2020.
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Leveraging UMLS-driven NLP to enhance identification of influenza predictors derived from electronic medical record data
Kari A. Stephens, Margaret A. Au, Meliha Yetisgen, Barry Lutz, Monica Zigman Suchsland, Mark H. Ebell, Matthew Thompson
bioRxiv 2020.04.24.058982; doi: https://doi.org/10.1101/2020.04.24.058982
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Leveraging UMLS-driven NLP to enhance identification of influenza predictors derived from electronic medical record data
Kari A. Stephens, Margaret A. Au, Meliha Yetisgen, Barry Lutz, Monica Zigman Suchsland, Mark H. Ebell, Matthew Thompson
bioRxiv 2020.04.24.058982; doi: https://doi.org/10.1101/2020.04.24.058982

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