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Open Access Research article

Identification of pneumonia and influenza deaths using the death certificate pipeline

Kailah Davis1, Catherine Staes1, Jeff Duncan2, Sean Igo13 and Julio C Facelli13*

Author Affiliations

1 Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA

2 Utah Department of Health, Salt Lake City, Utah, USA

3 Center for High Performance Computing, University of Utah, Salt Lake City, Utah, USA

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BMC Medical Informatics and Decision Making 2012, 12:37  doi:10.1186/1472-6947-12-37

Published: 8 May 2012

Abstract

Background

Death records are a rich source of data, which can be used to assist with public surveillance and/or decision support. However, to use this type of data for such purposes it has to be transformed into a coded format to make it computable. Because the cause of death in the certificates is reported as free text, encoding the data is currently the single largest barrier of using death certificates for surveillance. Therefore, the purpose of this study was to demonstrate the feasibility of using a pipeline, composed of a detection rule and a natural language processor, for the real time encoding of death certificates using the identification of pneumonia and influenza cases as an example and demonstrating that its accuracy is comparable to existing methods.

Results

A Death Certificates Pipeline (DCP) was developed to automatically code death certificates and identify pneumonia and influenza cases. The pipeline used MetaMap to code death certificates from the Utah Department of Health for the year 2008. The output of MetaMap was then accessed by detection rules which flagged pneumonia and influenza cases based on the Centers of Disease and Control and Prevention (CDC) case definition. The output from the DCP was compared with the current method used by the CDC and with a keyword search. Recall, precision, positive predictive value and F-measure with respect to the CDC method were calculated for the two other methods considered here. The two different techniques compared here with the CDC method showed the following recall/ precision results: DCP: 0.998/0.98 and keyword searching: 0.96/0.96. The F-measure were 0.99 and 0.96 respectively (DCP and keyword searching). Both the keyword and the DCP can run in interactive form with modest computer resources, but DCP showed superior performance.

Conclusion

The pipeline proposed here for coding death certificates and the detection of cases is feasible and can be extended to other conditions. This method provides an alternative that allows for coding free-text death certificates in real time that may increase its utilization not only in the public health domain but also for biomedical researchers and developers.

Trial Registration

This study did not involved any clinical trials.

Keywords:
Public health informatics; Natural language processing; Surveillance; Pneumonia and influenza