  Following the global COVID-19 pandemic, the number of scientific papers
studying the virus has grown massively, leading to increased interest in
automated literate review. We present a clinical text mining system that
improves on previous efforts in three ways. First, it can recognize over 100
different entity types including social determinants of health, anatomy, risk
factors, and adverse events in addition to other commonly used clinical and
biomedical entities. Second, the text processing pipeline includes assertion
status detection, to distinguish between clinical facts that are present,
absent, conditional, or about someone other than the patient. Third, the deep
learning models used are more accurate than previously available, leveraging an
integrated pipeline of state-of-the-art pretrained named entity recognition
models, and improving on the previous best performing benchmarks for assertion
status detection. We illustrate extracting trends and insights, e.g. most
frequent disorders and symptoms, and most common vital signs and EKG findings,
from the COVID-19 Open Research Dataset (CORD-19). The system is built using
the Spark NLP library which natively supports scaling to use distributed
clusters, leveraging GPUs, configurable and reusable NLP pipelines, healthcare
specific embeddings, and the ability to train models to support new entity
types or human languages with no code changes.
