DeepAI AI Chat
Log In Sign Up

Extracting COVID-19 Diagnoses and Symptoms From Clinical Text: A New Annotated Corpus and Neural Event Extraction Framework

by   Kevin Lybarger, et al.

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). In a secondary use application, we explored the prediction of COVID-19 test results using structured patient data (e.g. vital signs and laboratory results) and automatically extracted symptom information. The automatically extracted symptoms improve prediction performance, beyond structured data alone.


page 1

page 2

page 3

page 4


Extracting COVID-19 Events from Twitter

We present a corpus of 7,500 tweets annotated with COVID-19 events, incl...

Extracting Medication Changes in Clinical Narratives using Pre-trained Language Models

An accurate and detailed account of patient medications, including medic...

Secondary Use of Clinical Problem List Entries for Neural Network-Based Disease Code Assignment

Clinical information systems have become large repositories for semi-str...

Assessment of Amazon Comprehend Medical: Medication Information Extraction

In November 27, 2018, Amazon Web Services (AWS) released Amazon Comprehe...

Identifying ARDS using the Hierarchical Attention Network with Sentence Objectives Framework

Acute respiratory distress syndrome (ARDS) is a life-threatening conditi...