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A study of the transferability of influenza case detection systems between two large healthcare systems.
- Source :
-
PloS one [PLoS One] 2017 Apr 05; Vol. 12 (4), pp. e0174970. Date of Electronic Publication: 2017 Apr 05 (Print Publication: 2017). - Publication Year :
- 2017
-
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.<br />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.<br />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.<br />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.
- Subjects :
- Adolescent
Adult
Aged
Bayes Theorem
Child
Child, Preschool
Delivery of Health Care
Electronic Health Records
Emergency Service, Hospital
Humans
Infant
Infant, Newborn
Machine Learning
Middle Aged
Natural Language Processing
Reproducibility of Results
Young Adult
Decision Support Techniques
Influenza, Human diagnosis
Technology Transfer
Subjects
Details
- Language :
- English
- ISSN :
- 1932-6203
- Volume :
- 12
- Issue :
- 4
- Database :
- MEDLINE
- Journal :
- PloS one
- Publication Type :
- Academic Journal
- Accession number :
- 28380048
- Full Text :
- https://doi.org/10.1371/journal.pone.0174970