  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.

Computation and Language;Artificial Intelligence;Machine Learning