  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.
