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Coronavirus disease 2019 (COVID-19) is a global pandemic. Although much hasbeen learned about the novel coronavirus since its emergence, there are manyopen 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 thesequestions. Data-driven, automatic information extraction models are needed touse this text-encoded information in large-scale studies. This work presents anew clinical corpus, referred to as the COVID-19 Annotated Clinical Text (CACT)Corpus, which comprises 1,472 notes with detailed annotations characterizingCOVID-19 diagnoses, testing, and clinical presentation. We introduce aspan-based event extraction model that jointly extracts all annotatedphenomena, achieving high performance in identifying COVID-19 and symptomevents with associated assertion values (0.83-0.97 F1 for events and 0.73-0.79F1 for assertions). In a secondary use application, we explored the predictionof COVID-19 test results using structured patient data (e.g. vital signs andlaboratory results) and automatically extracted symptom information. Theautomatically extracted symptoms improve prediction performance, beyondstructured data alone.

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