Automatic detection of acute bacterial pneumonia from chest x-ray reports
Automatic detection of acute bacterial pneumonia from chest x-ray reports
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DOI：
10.1136/jamia.2000.0070593
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发表时间：
2000-11-01
影响因子：
6.4
通讯作者：
Haug, PJ
中科院分区：
文献类型：
--
作者：
Fiszman, M;Chapman, WW;Haug, PJ
目的：评价自然语言处理系统在从胸片报告中提取肺炎相关概念方面的性能。方法：设计：4名医生、3名非专业人员、一个自然语言处理系统、2个关键词搜索（指定为AAKS和KS）检测3个肺炎相关概念的存在与否，并从292例胸片报告中推断急性细菌性肺炎的存在与否。黄金标准：三名独立医生的多数票。测量了金标准的可靠性。结果测量：与金标准相关的召回率、精确度、特异性和一致性（使用Finn’s R统计量）。使用McNemar测试对每个肺炎概念和急性细菌性肺炎的疾病推断进行医生和其他受试者之间的差异进行测试。结果：标准品的信度范围为0.86 ~ 0.96。医生推断急性细菌性肺炎的召回率、精确度、特异性和一致性（Finn R）分别为0.94、0.87、0.91和0.84；自然语言处理系统为0.95、0.78、0.85、0.75；非专业人员为0.46、0.89、0.95和0.54；AAKS分别为0.79、0.63、0.71和0.49；KS分别为0.87、0.70、0.77、0.62。McNemar两两比较显示了一名医生与自然语言处理系统在浸润概念上的差异，以及另一名医生与自然语言处理系统在急性细菌性肺炎推理上的差异。比较还显示，大多数医生在所有肺炎概念和疾病推断上与其他受试者有显著差异。结论：在胸片报告肺炎相关概念提取中，自然语言处理系统的性能与医师相近，优于非专业人员和关键词搜索。编码的肺炎信息有可能支持我们机构使用的几种与肺炎相关的应用程序。这些应用包括一个被称为抗生素助手的决策支持系统，一个肺炎的计算机临床协议，以及一个在放射科的质量保证应用。
Objective: To evaluate the performance of a natural language processing system in extracting pneumonia-related concepts from chest x-ray reports.Methods: Design: Four physicians, three lay persons, a natural language processing system, and two keyword searches (designated AAKS and KS) detected the presence or absence of three pneumonia-related concepts and inferred the presence or absence of acute bacterial pneumonia from 292 chest x-ray reports. Gold standard: Majority vote of three independent physicians. Reliability of the gold standard was measured. Outcome measures: Recall, precision, specificity, and agreement (using Finn's R statistic) with respect to the gold standard. Differences between the physicians and the other subjects were tested using the McNemar test for each pneumonia concept and for the disease inference of acute bacterial pneumonia.Results: Reliability of the reference standard ranged from 0.86 to 0.96. Recall, precision, specificity, and agreement (Finn R) for the inference on acute bacterial pneumonia were, respectively, 0.94, 0.87, 0.91, and 0.84 for physicians; 0.95, 0.78, 0.85, and 0.75 for natural language processing system; 0.46, 0.89, 0.95, and 0.54 for lay persons; 0.79, 0.63, 0.71, and 0.49 for AAKS; and 0.87, 0.70, 0.77, and 0.62 for KS. The McNemar pairwise comparisons showed differences between one physician and the natural language processing system for the infiltrate concept and between another physician and the natural language processing system for the inference on acute bacterial pneumonia. The comparisons also showed that most physicians were significantly different from the other subjects in all pneumonia concepts and the disease inference.Conclusion: In extracting pneumonia related concepts from chest x-ray reports, the performance of the natural language processing system was similar to that of physicians and better than that of lay persons and keyword searches. The encoded pneumonia information has the potential to support several pneumonia-related applications used in our institution. The applications include a decision support system called the antibiotic assistant, a computerized clinical protocol for pneumonia, and a quality assurance application in the radiology department.