Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Germany.
Empolis Information Management, Kaiserslautern, Germany.
Int J Med Inform. 2020 May;137:104106. doi: 10.1016/j.ijmedinf.2020.104106. Epub 2020 Feb 29.
The majority of radiological reports are still written as free text and lack structure. Further evaluation of free-text reports is difficult to achieve without a great deal of manual effort, and is not possible in everyday clinical practice. This study aims to automatically capture clinical information and positive hit rates from narrative radiological reports of suspected urolithiasis using natural language processing (NLP).
Narrative reports of low dose computed tomography (CT) of the retroperitoneum from April 2016 to July 2018 (n = 1714) were analyzed using NLP. These free-text reports were automatically structured based on RadLex concepts. Manual feedback was used to test and train the NLP engine to further enhance the performance. The chi-squared test, phi coefficient, and logistic regression analysis were performed to determine the effect of clinical information on the positive hit rate of urolithiasis.
Urolithiasis was affirmed in 72 % of the reports; in 38 % at least one stone was described in the kidneys, and in 45 % at least one stone was described in the ureter. Clinical information, such as previous stone history and obstructive uropathy, showed a strong correlation with confirmed urolithiasis (p = 0.001). Previous stone history and the combination of obstructive uropathy and loin pain had the highest association with positive urolithiasis (p < 0.001).
Applying this NLP approach to already existing free-text reports allows the conversion of such reports into a structured form. This may be valuable for epidemiological studies, to evaluate the appropriateness of CT examinations, or to answer a variety of research questions.
大多数放射学报告仍以自由文本的形式呈现，缺乏结构。如果不进行大量的人工努力，进一步评估自由文本报告是很困难的，而且在日常临床实践中也是不可能的。本研究旨在使用自然语言处理（NLP）自动从疑似尿路结石的叙述性放射学报告中捕获临床信息和阳性检出率。
使用 NLP 对 2016 年 4 月至 2018 年 7 月的低剂量腹部 CT （1714 例）的叙述性报告进行分析。这些自由文本报告基于 RadLex 概念自动进行了结构化处理。使用手动反馈来测试和培训 NLP 引擎，以进一步提高性能。采用卡方检验、phi 系数和逻辑回归分析来确定临床信息对尿路结石阳性检出率的影响。
报告中 72%确认存在尿路结石；38%的报告至少在肾脏中描述了一颗结石，45%的报告至少在输尿管中描述了一颗结石。临床信息，如既往结石史和梗阻性尿路病，与确认的尿路结石有很强的相关性（p = 0.001）。既往结石史和梗阻性尿路病合并腰痛与阳性尿路结石的相关性最高（p < 0.001）。
将这种 NLP 方法应用于现有的自由文本报告中，可以将这些报告转换为结构化形式。这对于流行病学研究、评估 CT 检查的适宜性或回答各种研究问题可能很有价值。
