Extracting COVID-19 diagnoses and symptoms from clinical text: A new annotated corpus and neural event extraction framework.
Extracting COVID-19 diagnoses and symptoms from clinical text: A new annotated corpus and neural event extraction framework.
复制标题
DOI：
10.1016/j.jbi.2021.103761
复制
发表时间：
2021-05
影响因子：
4.5
通讯作者：
Yetisgen M
中科院分区：
文献类型：
--
作者：
Lybarger K;Ostendorf M;Thompson M;Yetisgen M
冠状病毒病2019年(新冠肺炎)是一场全球性的大流行。虽然自从这种新型冠状病毒出现以来，人们已经了解了很多，但在跟踪其传播、描述症状、预测感染的严重程度和预测医疗保健利用方面仍有许多悬而未决的问题。自由文本临床笔记包含解决这些问题的关键信息。为了在大规模研究中使用这种文本编码的信息，需要数据驱动的自动信息提取模型。这项工作提出了一个新的临床语料库，称为新冠肺炎注释临床文本语料库，包括1,472条注释，详细的注释特征的新冠肺炎诊断，测试和临床表现。我们提出了一个基于SPAN的事件抽取模型，它联合抽取所有标注的现象，在识别新冠肺炎和症状事件以及相关断言值(事件的F1为0.83-0.97F1，断言的F1为0.73-0.79F1)上取得了较高的性能。我们基于SPAN的事件提取模型的性能优于基于MetaMapLite构建的提取程序，用于识别带有断言值的症状。在二次使用应用中，我们使用结构化的患者数据(例如生命体征和实验室结果)预测新冠肺炎测试结果，并自动提取症状信息，以探索新冠肺炎的临床表现。自动提取的症状提高了新冠肺炎的预测性能，而不仅仅是结构化数据。
Coronavirus disease 2019 (COVID-19) is a global pandemic. Although much has been learned about the novel coronavirus since its emergence, there are many open questions related to tracking its spread, describing symptomology, predicting the severity of infection, and forecasting healthcare utilization. Free-text clinical notes contain critical information for resolving these questions. Data-driven, automatic information extraction models are needed to use this text-encoded information in large-scale studies. This work presents a new clinical corpus, referred to as the COVID-19 Annotated Clinical Text (CACT) Corpus, which comprises 1,472 notes with detailed annotations characterizing COVID-19 diagnoses, testing, and clinical presentation. We introduce a span-based event extraction model that jointly extracts all annotated phenomena, achieving high performance in identifying COVID-19 and symptom events with associated assertion values (0.83–0.97 F1 for events and 0.73–0.79 F1 for assertions). Our span-based event extraction model outperforms an extractor built on MetaMapLite for the identification of symptoms with assertion values. In a secondary use application, we predicted COVID-19 test results using structured patient data (e.g. vital signs and laboratory results) and automatically extracted symptom information, to explore the clinical presentation of COVID-19. Automatically extracted symptoms improve COVID-19 prediction performance, beyond structured data alone.
登录
查看更多内容
影响因子：
4.5
作者：
Bejan, Cosmin Adrian;Vanderwende, Lucy;Yetisgen-Yildiz, Meliha
通讯作者：
Yetisgen-Yildiz, Meliha
影响因子：
8.4
作者：
Amzat, Jimoh;Aminu, Kafayat;Danjibo, Maryann C.
通讯作者：
Danjibo, Maryann C.
用于联合实体和关系提取的深度神经网络模型
影响因子：
3.9
作者：
Pang, Yihe;Liu, Jie;Zhang, Kai
通讯作者：
Zhang, Kai
影响因子：
9.8
作者：
Johnson AE;Pollard TJ;Shen L;Lehman LW;Feng M;Ghassemi M;Moody B;Szolovits P;Celi LA;Mark RG
通讯作者：
Mark RG
影响因子：
1.8
作者：
Kim, Ji-Hyun
通讯作者：
Kim, Ji-Hyun