期刊
COMPUTERS IN BIOLOGY AND MEDICINE
卷 37, 期 3, 页码 296-304出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2006.02.001
关键词
data mining; natural language processing; similarity metrics; electronic medical records; classification; community-acquired pneumonia; discordance
类别
资金
- NLM NIH HHS [R01 LM06910, R01 LM006910] Funding Source: Medline
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). (c) 2006 Elsevier Ltd. All rights reserved.
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