Extracting Concepts for Precision Oncology from the Biomedical
  Literature
  This paper describes an initial dataset and automatic natural language
processing (NLP) method for extracting concepts related to precision oncology
from biomedical research articles. We extract five concept types: Cancer,
Mutation, Population, Treatment, Outcome. A corpus of 250 biomedical abstracts
were annotated with these concepts following standard double-annotation
procedures. We then experiment with BERT-based models for concept extraction.
The best-performing model achieved a precision of 63.8%, a recall of 71.9%, and
an F1 of 67.1. Finally, we propose additional directions for research for
improving extraction performance and utilizing the NLP system in downstream
precision oncology applications.
