自然语言处理能准确地对结肠镜检查和病理报告的结果进行分类。
Natural language processing accurately categorizes findings from colonoscopy and pathology reports.
机构信息
Division of Gastroenterology and Hepatology, Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA.
出版信息
Clin Gastroenterol Hepatol. 2013 Jun;11(6):689-94. doi: 10.1016/j.cgh.2012.11.035. Epub 2013 Jan 11.
BACKGROUND & AIMS: Little is known about the ability of natural language processing (NLP) to extract meaningful information from free-text gastroenterology reports for secondary use.
METHODS
We randomly selected 500 linked colonoscopy and pathology reports from 10,798 nonsurveillance colonoscopies to train and test the NLP system. By using annotation by gastroenterologists as the reference standard, we assessed the accuracy of an open-source NLP engine that processed and extracted clinically relevant concepts. The primary outcome was the highest level of pathology. Secondary outcomes were location of the most advanced lesion, largest size of an adenoma removed, and number of adenomas removed.
RESULTS
The NLP system identified the highest level of pathology with 98% accuracy, compared with triplicate annotation by gastroenterologists (the standard). Accuracy values for location, size, and number were 97%, 96%, and 84%, respectively.
CONCLUSIONS
The NLP can extract specific meaningful concepts with 98% accuracy. It might be developed as a method to further quantify specific quality metrics.
背景与目的
关于自然语言处理（NLP）从肠胃病学报告的自由文本中提取有意义信息以进行二次利用的能力，人们知之甚少。
方法
我们随机选择了 10798 例非监测性结肠镜检查中的 500 例结肠镜检查和病理报告进行培训和测试，以评估一种开源的 NLP 引擎对临床相关概念的处理和提取的准确性。该引擎使用胃肠病学家的注释作为参考标准。主要结果是最高级别的病理学。次要结果是最先进病变的位置、切除的最大腺瘤大小和切除的腺瘤数量。
结果
NLP 系统识别最高级别的病理学的准确率为 98%，而胃肠病学家的三重注释（标准）准确率为 97%。位置、大小和数量的准确率分别为 97%、96%和 84%。
结论
NLP 可以以 98%的准确率提取特定的有意义的概念。它可能被开发为进一步量化特定质量指标的方法。
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