Extracting Concepts for Precision Oncology from the Biomedical Literature

09/30/2020

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by   Nicholas Greenspan, et al.

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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 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.