Using discordance to improve classification in narrative clinical databases: an application to community-acquired pneumonia.

Using discordance to improve classification in narrative clinical databases: an application to community-acquired pneumonia. Research Abstract Details 

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Using discordance to improve classification in narrative clinical databases: an application to community-acquired pneumonia. Abstract Text:

George Hripcsak,Charles Knirsch,Li Zhou,Adam Wilcox,Genevieve B Melton,

Data mining in electronic medical records may facilitate clinical research, but much of the structured data may be miscoded, incomplete, or non-specific. The exploitation of narrative data using natural language processing may help, although nesting, varying granularity, and repetition remain challenges. In a study of community-acquired pneumonia using electronic records, these issues led to poor classification. Limiting queries to accurate, complete records led to vastly reduced, possibly biased samples. We exploited knowledge latent in the electronic records to improve classification. A similarity metric was used to cluster cases. We defined discordance as the degree to which cases within a cluster give different answers for some query that addresses a classification task of interest. Cases with higher discordance are more likely to be incorrectly classified, and can be reviewed manually to adjust the classification, improve the query, or estimate the likely accuracy of the query. In a study of pneumonia--in which the ICD9-CM coding was found to be very poor--the discordance measure was statistically significantly correlated with classification correctness (.45; 95% CI .15-.62).

Using discordance to improve classification in narrative clinical databases: an application to community-acquired pneumonia. Publishing Authors By Initials

G Hripcsak,C Knirsch,L Zhou,A Wilcox,GB Melton,

For similar bacterial infections and mycoses: bacterial infections: pneumonia, bacterial research abstracts see: bacterial infections and mycoses: bacterial infections: pneumonia, bacterial research

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Using discordance to improve classification in narrative clinical databases: an application to community-acquired pneumonia. Journal Published:

PUBLICATION TYPE: Research Support, Non-U.S. Gov

Journal: Computers in biology and medicine

VOLUME: 37

Page Numbers: 296-304

Journal Abbreviation: Comput. Biol. Med.

ISSN: 0010-4825

DAY: 18

MONTH: 04

YEAR: 2006

Using discordance to improve classification in narrative clinical databases: an application to community-acquired pneumonia. Information

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LANGUAGE: eng

NlmUniqueID: 1250250

Using discordance to improve classification in narrative clinical databases: an application to community-acquired pneumonia. Keywords Mesh Terms:

KEYWORDS: Pneumonia, Bacterial

MESH TERMS: diagnosis

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Grant and Affiliation Information for Using discordance to improve classification in narrative clinical databases: an application to community-acquired pneumonia.

AFFILIATION: Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA. hripcsak@columbia.edu

Country: United States

AGENCY: United States NLM

GRANT: R01 LM06910

ACRONYM: LM

MEDLINETA: Comput Biol Med

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