Identification of pneumonia and influenza deaths using the death certificate pipeline

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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.

Related collections

Most cited references 15

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National Hospital Ambulatory Medical Care Survey: 2007 emergency department summary.

Richard Niska, Jianmin Xu, Farida Bhuiya (2010)

This report presents data on U.S. emergency department (ED) visits in 2007, with statistics on hospital, patient, and visit characteristics. Data are from the 2007 National Hospital Ambulatory Medical Care Survey, which uses a national probability sample of visits to emergency departments of nonfederal general and short-stay hospitals in the United States. Sample data were weighted to produce annual national estimates. In 2007, there were about 117 million ED visits in the United States. About 25 percent of visits were covered by Medicaid or the State Children's Health Insurance Program (SCHIP). About one-fifth of ED visits by children younger than 15 years of age were to pediatric EDs. There were 121 ED visits for asthma per 10,000 children under 5 years of age. The leading injury-related cause of ED visits was unintentional falls. Two percent of visits resulted in admission to an observation unit. Electronic medical records were used in 62 percent of EDs.

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Prevention and control of seasonal influenza with vaccines: recommendations of the Advisory Committee on Immunization Practices (ACIP), 2009.

E Fiore, G. Mootrey, George N. Cox … (2009)

This report updates the 2008 recommendations by CDC's Advisory Committee on Immunization Practices (ACIP) regarding the use of influenza vaccine for the prevention and control of seasonal influenza (CDC. Prevention and control of influenza: recommendations of the Advisory Committee on Immunization Practices [ACIP]. MMWR 2008;57[No. RR-7]). Information on vaccination issues related to the recently identified novel influenza A H1N1 virus will be published later in 2009. The 2009 seasonal influenza recommendations include new and updated information. Highlights of the 2009 recommendations include 1) a recommendation that annual vaccination be administered to all children aged 6 months-18 years for the 2009-10 influenza season; 2) a recommendation that vaccines containing the 2009-10 trivalent vaccine virus strains A/Brisbane/59/2007 (H1N1)-like, A/Brisbane/10/2007 (H3N2)-like, and B/Brisbane/60/2008-like antigens be used; and 3) a notice that recommendations for influenza diagnosis and antiviral use will be published before the start of the 2009-10 influenza season. Vaccination efforts should begin as soon as vaccine is available and continue through the influenza season. Approximately 83% of the United States population is specifically recommended for annual vaccination against seasonal influenza; however, <40% of the U.S. population received the 2008-09 influenza vaccine. These recommendations also include a summary of safety data for U.S. licensed influenza vaccines. These recommendations and other information are available at CDC's influenza website (http://www.cdc.gov/flu); any updates or supplements that might be required during the 2009-10 influenza season also can be found at this website. Vaccination and health-care providers should be alert to announcements of recommendation updates and should check the CDC influenza website periodically for additional information.

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Automated encoding of clinical documents based on natural language processing.

Carol Friedman, Lyudmila Shagina, Yves Lussier … (2015)

The aim of this study was to develop a method based on natural language processing (NLP) that automatically maps an entire clinical document to codes with modifiers and to quantitatively evaluate the method. An existing NLP system, MedLEE, was adapted to automatically generate codes. The method involves matching of structured output generated by MedLEE consisting of findings and modifiers to obtain the most specific code. Recall and precision applied to Unified Medical Language System (UMLS) coding were evaluated in two separate studies. Recall was measured using a test set of 150 randomly selected sentences, which were processed using MedLEE. Results were compared with a reference standard determined manually by seven experts. Precision was measured using a second test set of 150 randomly selected sentences from which UMLS codes were automatically generated by the method and then validated by experts. Recall of the system for UMLS coding of all terms was .77 (95% CI.72-.81), and for coding terms that had corresponding UMLS codes recall was .83 (.79-.87). Recall of the system for extracting all terms was .84 (.81-.88). Recall of the experts ranged from .69 to .91 for extracting terms. The precision of the system was .89 (.87-.91), and precision of the experts ranged from .61 to .91. Extraction of relevant clinical information and UMLS coding were accomplished using a method based on NLP. The method appeared to be comparable to or better than six experts. The advantage of the method is that it maps text to codes along with other related information, rendering the coded output suitable for effective retrieval.

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Author and article information

Journal

Journal ID (nlm-ta): BMC Med Inform Decis Mak

Journal ID (iso-abbrev): BMC Med Inform Decis Mak

Title: BMC Medical Informatics and Decision Making

Publisher: BioMed Central

ISSN (Electronic): 1472-6947

Publication date Collection: 2012

Publication date (Electronic): 8 May 2012

Volume: 12

Page: 37

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

Article

Publisher ID: 1472-6947-12-37

DOI: 10.1186/1472-6947-12-37

PMC ID: 3444937

PubMed ID: 22569097

SO-VID: 36d47282-b40a-40e9-a493-6387a0e8790b

License:

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Identification of pneumonia and influenza deaths using the death certificate pipeline

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Abstract

Background

Results

Conclusion

Trial Registration

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Radiology and Natural Language Processing

Most cited references 15

National Hospital Ambulatory Medical Care Survey: 2007 emergency department summary.

Prevention and control of seasonal influenza with vaccines: recommendations of the Advisory Committee on Immunization Practices (ACIP), 2009.

Automated encoding of clinical documents based on natural language processing.

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