Extraction of recommendation features in radiology with natural language processing: Exploratory study
Extraction of recommendation features in radiology with natural language processing: Exploratory study
复制标题
DOI：
10.2214/ajr.07.3508
复制
发表时间：
2008-08-01
影响因子：
5
通讯作者：
Dreyer, Keith J.
中科院分区：
文献类型：
--
作者：
Dang, Pragya A.;Kalra, Mannudeep K.;Dreyer, Keith J.
OBJECTIVE.本研究的目的是验证一个自然语言处理程序的建议功能，如推荐的时间范围和成像技术，从电子放射学报告的提取和评估模式的建议功能在一个大型数据库的放射学reports.MATERIALS AND METHODS。本研究是在1995-2004年的放射学报告数据库中进行的。从该数据库中，随机选择了120份有或无建议的报告。两名放射科医生根据建议、时间范围和建议随访或重复检查的成像技术对这些报告进行独立分类。然后，使用自然语言处理程序根据放射科医生使用的相同标准对报告进行分类。确定推荐特征分类的准确性。然后，该程序用于确定不同患者的推荐特征模式和整个数据库中4，211，503份报告的成像特征。自然语言处理程序识别放射科医师推荐的成像技术以供进一步评价的准确率为93.2%（82/88）。报告中建议的时间范围与程序获得的88项建议进行分类，导致83项（94.3%）准确分类和5项（5.7%）不准确分类。CT的推荐最常见（27.9%，376，918份报告中的105，076份），其次是MRI（17.8%）。然而，在大多数（85.4%，322，074/376，918）有影像学建议的报告中，放射科医生没有指定时间范围。在放射学报告的大型数据库中准确确定推荐的成像技术和时间范围是可能的，使用自然语言处理程序。大多数影像学建议是高成本，但更准确的放射学研究。
OBJECTIVE. The purposes of this study were to validate a natural language processing program for extraction of recommendation features, such as recommended time frames and imaging technique, from electronic radiology reports and to assess patterns of recommendation features in a large database of radiology reports.MATERIALS AND METHODS. This study was performed on a radiology reports database covering the years 1995-2004. From this database, 120 reports with and without recommendations were selected and randomized. Two radiologists independently classified these reports according to presence of recommendations, time frame, and imaging technique suggested for follow-up or repeated examinations. The natural language processing program then was used to classify the reports according to the same criteria used by the radiologists. The accuracy of classification of recommendation features was determined. The program then was used to determine the patterns of recommendation features for different patients and imaging features in the entire database of 4,211,503 reports.RESULTS. The natural language processing program had an accuracy of 93.2% (82/88) for identifying the imaging technique recommended by the radiologists for further evaluation. Categorization of recommended time frames in the reports with the 88 recommendations obtained with the program resulted in 83 (94.3%) accurate classifications and five (5.7%) inaccurate classifications. Recommendations of CT were most common (27.9%, 105,076 of 376,918 reports) followed by those for MRI (17.8%). In most (85.4%, 322,074/376,918) of the reports with imaging recommendations, however, radiologists did not specify the time frame.CONCLUSION. Accurate determination of recommended imaging techniques and time frames in a large database of radiology reports is possible with a natural language processing program. Most imaging recommendations are for high-cost but more accurate radiologic studies.