人群放射学报告内容的变化可以发现新出现的疾病。在这里,我们开发了一种方法,使用自然语言处理来量化放射学报告的连续时间分组的相似性,我们调查了连续时期之间差异的出现是否与法国COVID-19大流行的开始有关。收集了2019年10月至2020年3月期间法国62个急诊科的67,368名连续成年人的CT报告。使用一克的时频逆文档频率(TF-IDF)分析对报告进行矢量化。对于每个连续的2周期间,我们基于TF-IDF值和分区-环绕-medoids对报告进行了无监督聚类.接下来,我们根据平均校正Rand指数(AARI)评估了该聚类与两周前的聚类之间的相似性.统计分析包括(1)互相关函数(CCF)与SARS-CoV-2阳性测试的数量和流感综合征的高级卫生指数(ASI-流感,来自开源数据集),(2)时间序列在不同滞后的线性回归,以了解AARI随时间的变化。总的来说,分析13235例胸部CT报告。AARI在滞后=1、5和6周与ASI流感相关(分别为P=0.0454、0.0121和0.0042),在滞后=-1和0周与SARS-CoV-2阳性测试相关(分别为P=0.0057和0.0001)。在最适合的情况下,AARI与ASI流感相关,滞后2周(P=0.0026),同一周SARS-CoV-2阳性检测(P<0.0001)及其相互作用(P<0.0001)(调整后的R2=0.921)。因此,我们的方法能够自动监测放射学报告的变化,并有助于捕获疾病的出现.
Changes in the content of radiological
reports at population level could detect emerging diseases. Herein, we developed a method to quantify similarities in consecutive temporal groupings of radiological
reports using natural language processing, and we investigated whether appearance of dissimilarities between consecutive periods correlated with the beginning of the COVID-19 pandemic in France. CT reports from 67,368 consecutive adults across 62 emergency departments throughout France between October 2019 and March 2020 were collected.
Reports were vectorized using time frequency-inverse document frequency (TF-IDF) analysis on one-grams. For each successive 2-week period, we performed unsupervised clustering of the
reports based on TF-IDF values and partition-around-medoids. Next, we assessed the similarities between this clustering and a clustering from two weeks before according to the average adjusted Rand index (AARI). Statistical analyses included (1) cross-correlation functions (CCFs) with the number of positive SARS-CoV-2 tests and advanced sanitary index for flu syndromes (ASI-flu, from open-source dataset), and (2) linear regressions of time series at different lags to understand the variations of AARI over time. Overall, 13,235 chest CT
reports were analyzed. AARI was correlated with ASI-flu at lag = + 1, + 5, and + 6 weeks (P = 0.0454, 0.0121, and 0.0042, respectively) and with SARS-CoV-2 positive tests at lag = - 1 and 0 week (P = 0.0057 and 0.0001, respectively). In the best fit, AARI correlated with the ASI-flu with a lag of 2 weeks (P = 0.0026), SARS-CoV-2-positive tests in the same week (P < 0.0001) and their interaction (P < 0.0001) (adjusted R2 = 0.921). Thus, our method enables the automatic monitoring of changes in radiological reports and could help capturing disease emergence.