Automated Classification of Radiology Reports for Acute Lung Injury: Comparison of Keyword and Machine Learning Based Natural Language Processing Approaches.
Automated Classification of Radiology Reports for Acute Lung Injury: Comparison of Keyword and Machine Learning Based Natural Language Processing Approaches.
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DOI：
10.1109/bibmw.2009.5332081
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发表时间：
2009-11
期刊：
影响因子：
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通讯作者：
Wurfel MM
中科院分区：
文献类型：
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作者：
Solti I;Cooke CR;Xia F;Wurfel MM
本文比较了急性肺损伤（ALI）的关键字和基于机器学习的胸部X射线报告分类的性能。ALI死亡率约为30%。高死亡率在一定程度上是手动胸部X线分类延迟的结果。自动化系统可以减少识别ALI的时间，并降低死亡率。在我们的研究中，两个语料库中的96和857份胸部X线报告由领域专家标记为ALI。我们开发了一个关键字和一个基于最大熵的分类系统。单词一元语法和字符n元语法为机器学习系统提供了特征。在857份报告语料库上，最大熵算法（字符为6元）取得了最高的性能（召回率=0.91，精度=0.90和F-测度=0.91）。这项研究表明，对于ALI胸部X射线报告的分类，机器学习方法上级基于关键字的系统，并实现了与最高性能的医生注释器相当的结果。
This paper compares the performance of keyword and machine learning-based chest x-ray report classification for Acute Lung Injury (ALI). ALI mortality is approximately 30 percent. High mortality is, in part, a consequence of delayed manual chest x-ray classification. An automated system could reduce the time to recognize ALI and lead to reductions in mortality. For our study, 96 and 857 chest x-ray reports in two corpora were labeled by domain experts for ALI. We developed a keyword and a Maximum Entropy-based classification system. Word unigram and character n-grams provided the features for the machine learning system. The Maximum Entropy algorithm with character 6-gram achieved the highest performance (Recall=0.91, Precision=0.90 and F-measure=0.91) on the 857-report corpus. This study has shown that for the classification of ALI chest x-ray reports, the machine learning approach is superior to the keyword based system and achieves comparable results to highest performing physician annotators.