Analysis of Stroke Detection during the COVID-19 Pandemic Using Natural Language Processing of Radiology Reports
Analysis of Stroke Detection during the COVID-19 Pandemic Using Natural Language Processing of Radiology Reports
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DOI：
10.3174/ajnr.a6961
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发表时间：
2021-03-01
影响因子：
3.5
通讯作者：
Kalpathy-Cramer, J.
中科院分区：
文献类型：
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作者：
Li, M. D.;Lang, M.;Kalpathy-Cramer, J.
背景和目的：冠状病毒病2019(新冠肺炎)大流行导致神经影像体积减少。我们的目标是使用放射学报告的自然语言处理来量化大流行期间CT或MR成像检测到的急性或亚急性缺血性卒中的变化。材料和方法：我们回顾分析了2017年3月1日至2020年4月30日期间从一个综合性卒中中心进行的32,555份来自脑CT和磁共振成像的放射学报告，涉及20,414名独特的患者。为了在自由文本报告中检测急性或亚急性缺血性中风，我们使用1987年随机抽样的带有手动注释的放射学报告训练了一个随机森林自然语言处理分类器。结果：自然语言处理分类器的5倍交叉验证分类准确率为0.97，F1得分为0.74，在交叉验证中对急性或亚急性缺血性卒中的实际数量略有低估(？5%)。重要的是，按年份分层的交叉验证绩效相似。将分类器应用于完整的研究队列，我们发现，与2017-2019年3月至2019年这几个月的平均水平相比，2020年3月至4月期间CT或MR成像报告的急性或亚急性缺血性中风患者的人数估计减少了24%。在有卒中相关顺序指征的患者中，接受神经影像检查发现急性或亚急性缺血性卒中的估计比例从2017-2019年的16%显著增加到2020年的21%(P=0.01)。自然语言处理分类器在外部数据上的表现较差。结论：神经影像检测到的急性或亚急性缺血性中风病例在新冠肺炎大流行期间有所下降，尽管订购的中风研究中急性或亚急性缺血性中风的阳性比例较高。自然语言处理方法可以帮助为流行病学研究自动跟踪急性或亚急性缺血性中风的数字，尽管由于放射科医生报告风格的差异，本地分类器培训是重要的。
BACKGROUND AND PURPOSE:The coronavirus disease 2019 (COVID-19) pandemic has led to decreases in neuroimaging volume. Our aim was to quantify the change in acute or subacute ischemic strokes detected on CT or MR imaging during the pandemic using natural language processing of radiology reports.MATERIALS AND METHODS:We retrospectively analyzed 32,555 radiology reports from brain CTs and MRIs from a comprehensive stroke center, performed from March 1 to April 30 each year from 2017 to 2020, involving 20,414 unique patients. To detect acute or subacute ischemic stroke in free-text reports, we trained a random forest natural language processing classifier using 1987 randomly sampled radiology reports with manual annotation. Natural language processing classifier generalizability was evaluated using 1974 imaging reports from an external dataset.RESULTS:The natural language processing classifier achieved a 5-fold cross-validation classification accuracy of 0.97 and an F1 score of 0.74, with a slight underestimation (?5%) of actual numbers of acute or subacute ischemic strokes in cross-validation. Importantly, cross-validation performance stratified by year was similar. Applying the classifier to the complete study cohort, we found an estimated 24% decrease in patients with acute or subacute ischemic strokes reported on CT or MR imaging from March to April 2020 compared with the average from those months in 2017?2019. Among patients with stroke-related order indications, the estimated proportion who underwent neuroimaging with acute or subacute ischemic stroke detection significantly increased from 16% during 2017?2019 to 21% in 2020 (P?=?.01). The natural language processing classifier performed worse on external data.CONCLUSIONS:Acute or subacute ischemic stroke cases detected by neuroimaging decreased during the COVID-19 pandemic, though a higher proportion of studies ordered for stroke were positive for acute or subacute ischemic strokes. Natural language processing approaches can help automatically track acute or subacute ischemic stroke numbers for epidemiologic studies, though local classifier training is important due to radiologist reporting style differences.