从癌症相关的自由文本放射学报告中自动提取和标准化检查结果。
Automated extraction and normalization of findings from cancer-related free-text radiology reports.
作者信息
Mamlin Burke W, Heinze Daniel T, McDonald Clement J
机构信息
Regenstrief Institute for Health Care, Indianapolis, Indiana, USA.
出版信息
AMIA Annu Symp Proc. 2003;2003:420-4.
UNLABELLED
We describe the performance of a particular natural language processing system that uses knowledge vectors to extract findings from radiology reports. LifeCode (A-Life Medical, Inc.) has been successfully coding reports for billing purposes for several years. In this study, we describe the use of LifeCode to code all findings within a set of 500 cancer-related radiology reports against a test set in which all findings were manually tagged. The system was trained with 1400 reports prior to running the test set.
RESULTS
LifeCode had a recall of 84.5% and precision of 95.7% in the coding of cancer-related radiology report findings.
CONCLUSION
Despite the use of a modest sized training set and minimal training iterations, when applied to cancer-related reports the system achieved recall and precision measures comparable to other reputable natural language processors in this domain.
未标注
我们描述了一种特定的自然语言处理系统的性能，该系统使用知识向量从放射学报告中提取结果。LifeCode（A-Life Medical公司）多年来一直成功地为计费目的对报告进行编码。在本研究中，我们描述了使用LifeCode对一组500份癌症相关放射学报告中的所有结果进行编码，并与一个所有结果都经过手动标注的测试集进行对比。在运行测试集之前，该系统用1400份报告进行了训练。
结果
LifeCode在癌症相关放射学报告结果编码中的召回率为84.5%，精确率为95.7%。
结论
尽管使用的训练集规模适中且训练迭代次数较少，但当应用于癌症相关报告时，该系统实现的召回率和精确率指标与该领域其他知名的自然语言处理器相当。
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