The surging amount of biomedical literature & digital clinical records
presents a growing need for text mining techniques that can not only identify
but also semantically relate entities in unstructured data. In this paper we
propose a text mining framework comprising of Named Entity Recognition (NER)
and Relation Extraction (RE) models, which expands on previous work in three
main ways. First, we introduce two new RE model architectures -- an
accuracy-optimized one based on BioBERT and a speed-optimized one utilizing
crafted features over a Fully Connected Neural Network (FCNN). Second, we
evaluate both models on public benchmark datasets and obtain new
state-of-the-art F1 scores on the 2012 i2b2 Clinical Temporal Relations
challenge (F1 of 73.6, +1.2% over the previous SOTA), the 2010 i2b2 Clinical
Relations challenge (F1 of 69.1, +1.2%), the 2019 Phenotype-Gene Relations
dataset (F1 of 87.9, +8.5%), the 2012 Adverse Drug Events Drug-Reaction dataset
(F1 of 90.0, +6.3%), and the 2018 n2c2 Posology Relations dataset (F1 of 96.7,
+0.6%). Third, we show two practical applications of this framework -- for
building a biomedical knowledge graph and for improving the accuracy of mapping
entities to clinical codes. The system is built using the Spark NLP library
which provides a production-grade, natively scalable, hardware-optimized,
trainable & tunable NLP framework.

该文介绍了一种文本挖掘框架，利用 Named Entity Recognition 和 Relation Extraction 模型对医学文献和数字临床记录进行处理，取得优异的实验结果，并构建了一个生物医学知识图谱。系统使用 Spark NLP 库提供的生产级、可伸缩、硬件优化、可训练和可调整的 NLP 框架。

关系抽取在临床文档理解中的应用

Deeper Clinical Document Understanding Using Relation Extraction

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。