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.

该研究构建了一个临床文本挖掘系统，利用命名实体识别和深度学习模型优化了现有技术，能够从 COVID-19 研究数据集中提取潜在趋势和洞见，支持分布式集群计算以及新增实体类型或人类语言训练模型无需编程。