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
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DOI：
10.1016/j.compbiomed.2006.02.001
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发表时间：
2007-03-01
影响因子：
7.7
通讯作者：
Melton, Genevieve B.
中科院分区：
文献类型：
--
作者：
Hripcsak, George;Knirsch, Charles;Melton, Genevieve B.
电子病历中的数据挖掘可能会促进临床研究，但许多结构化数据可能是错误编码、不完整或非特定的。使用自然语言处理对叙事数据的开发可能会有所帮助，尽管嵌套、不同的粒度和重复仍然是挑战。在一项使用电子记录对社区获得性肺炎进行的研究中，这些问题导致分类不佳。将查询限制在准确、完整的记录上会导致样本数量大大减少，可能存在偏见。我们利用电子记录中隐藏的知识来改进分类。使用相似度度量对案例进行聚类。我们将不一致性定义为集群内的案例对涉及感兴趣的分类任务的某个查询给出不同答案的程度。不一致性越高的案例更有可能被错误分类，并且可以手动检查以调整分类、改进查询或估计查询的可能准确性。在肺炎的一项研究中，ICD9-CM编码被发现非常差-不一致性度量与分类正确性在统计学上显著相关(0.45；95%可信区间.15-.62)。(C)2006爱思唯尔有限公司。保留所有权利。
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.