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PLoS One. 2017 Apr 5;12(4):e0174970. doi: 10.1371/journal.pone.0174970. eCollection 2017.

A study of the transferability of influenza case detection systems between two large healthcare systems.

Author information

1
Real-time Outbreak and Disease Surveillance Laboratory, Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
2
Intelligent Systems Program, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
3
Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States of America.
4
Intermountain Healthcare, Salt Lake City, Utah, United States of America.
5
Department of Pediatrics, University of Utah, Salt Lake City, Utah, United States of America.
6
Department of Pediatrics, Children's Hospital of Pittsburgh of UPMC, Pittsburgh, Pennsylvania, United States of America.
7
Department of Genetics, Stanford University School of Medicine, Stanford, California, United States of America.
8
VA Salt Lake City Healthcare System, Salt Lake City, Utah, United States of America.

Abstract

OBJECTIVES:

This study evaluates the accuracy and transferability of Bayesian case detection systems (BCD) that use clinical notes from emergency department (ED) to detect influenza cases.

METHODS:

A BCD uses natural language processing (NLP) to infer the presence or absence of clinical findings from ED notes, which are fed into a Bayesain network classifier (BN) to infer patients' diagnoses. We developed BCDs at the University of Pittsburgh Medical Center (BCDUPMC) and Intermountain Healthcare in Utah (BCDIH). At each site, we manually built a rule-based NLP and trained a Bayesain network classifier from over 40,000 ED encounters between Jan. 2008 and May. 2010 using feature selection, machine learning, and expert debiasing approach. Transferability of a BCD in this study may be impacted by seven factors: development (source) institution, development parser, application (target) institution, application parser, NLP transfer, BN transfer, and classification task. We employed an ANOVA analysis to study their impacts on BCD performance.

RESULTS:

Both BCDs discriminated well between influenza and non-influenza on local test cases (AUCs > 0.92). When tested for transferability using the other institution's cases, BCDUPMC discriminations declined minimally (AUC decreased from 0.95 to 0.94, p<0.01), and BCDIH discriminations declined more (from 0.93 to 0.87, p<0.0001). We attributed the BCDIH decline to the lower recall of the IH parser on UPMC notes. The ANOVA analysis showed five significant factors: development parser, application institution, application parser, BN transfer, and classification task.

CONCLUSION:

We demonstrated high influenza case detection performance in two large healthcare systems in two geographically separated regions, providing evidentiary support for the use of automated case detection from routinely collected electronic clinical notes in national influenza surveillance. The transferability could be improved by training Bayesian network classifier locally and increasing the accuracy of the NLP parser.

PMID:
28380048
PMCID:
PMC5381795
DOI:
10.1371/journal.pone.0174970
[Indexed for MEDLINE]
Free PMC Article

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