UNLOCKING CLINICAL-DATA FROM NARRATIVE REPORTS - A STUDY OF NATURAL-LANGUAGE PROCESSING
UNLOCKING CLINICAL-DATA FROM NARRATIVE REPORTS - A STUDY OF NATURAL-LANGUAGE PROCESSING
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DOI：
10.7326/0003-4819-122-9-199505010-00007
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发表时间：
1995-05-01
影响因子：
39.2
通讯作者：
CLAYTON, PD
中科院分区：
文献类型：
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作者：
HRIPCSAK, G;FRIEDMAN, C;CLAYTON, PD
目的：评估叙述性报告中描述的临床状况的自动检测。设计：自动化方法和人类专家检测200份入院胸片报告中6种临床状况的存在或不存在。研究对象：计算机化的通用自然语言处理器; 6名内科医生; 6名放射科医生; 6名非专业人员;和3种其他计算机方法。主要结果测量：受试者之间的分歧被量化为“距离"结果：采用多数投票法，医生在200份报告中发现了101种疾病最常见的疾病是急性细菌性肺炎（患病率，0.14），最不常见的是慢性阻塞性肺病（患病率，0.03）。平均20%的报告中，医生对至少1种疾病的存在意见不一致。医生之间的平均受试者间距离为0.24（95% CI，0.19至0.29），最大可能距离为6。没有医生的距离明显大于平均值。自然语言处理器与医生的平均距离为0.26（CI，0.21至0.32;不显著大于医生的平均值）。外行人和替代计算机方法与医生的距离显著更大（均>0.5）。自然语言处理器的灵敏度为81%，（CI，73%至87%），特异性为98%（CI，97%至99%）;医生的平均敏感性为85%，平均特异性为98%。医生们对叙述性报告的解释意见不一，但这并不是由离群的医生或内科医生和放射科医生阅读报告的方式的一致差异引起的。自然语言处理器与医生没有区别，并且上级所有其他比较对象。虽然这项研究的领域受到限制（胸部X光片中的六种临床状况），但自然语言处理似乎有可能以支持自动决策支持和临床研究的方式从叙述性报告中提取临床信息。
Objective: To evaluate the automated detection of clinical conditions described in narrative reports.Design: Automated methods and human experts detected the presence or absence of six clinical conditions in 200 admission chest radiograph reports.Study Subjects: A computerized, general-purpose natural language processor; 6 internists; 6 radiologists; 6 lay persons; and 3 other computer methods.Main Outcome Measures: Intersubject disagreement was quantified by ''distance'' (the average number of clinical conditions per report on which two subjects disagreed) and by sensitivity and specificity with respect to the physicians.Results: Using a majority vote, physicians detected 101 conditions in the 200 reports (0.51 per report); the most common condition was acute bacterial pneumonia (prevalence, 0.14), and the least common was chronic obstructive pulmonary disease (prevalence, 0.03). Pairs of physicians disagreed on the presence of at least 1 condition for an average of 20% of reports. The average intersubject distance among physicians was 0.24 (95% CI, 0.19 to 0.29) out of a maximum possible distance of 6. No physician had a significantly greater distance than the average. The average distance of the natural language processor from the physicians was 0.26 (CI, 0.21 to 0.32; not significantly greater than the average among physicians). Lay persons and alternative computer methods had significantly greater distance from the physicians (all >0.5). The natural language processor had a sensitivity of 81% (CI, 73% to 87%) and a specificity of 98% (CI, 97% to 99%); physicians had an average sensitivity of 85% and an average specificity of 98%.Conclusions: Physicians disagreed on the interpretation of narrative reports, but this was not caused by outlier physicians or a consistent difference in the way internists and radiologists read reports. The natural language processor was not distinguishable from the physicians and was superior to all other comparison subjects. Although the domain of this study was restricted (six clinical conditions in chest radiographs), natural language processing seems to have the potential to extract clinical information from narrative reports in a manner that will support automated decision-support and clinical research.