Extracting Concepts for Precision Oncology from the Biomedical Literature

Nicholas Greenspan; Yuqi Si; Kirk Roberts

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arXiv · 2010.00074

Extracting Concepts for Precision Oncology from the Biomedical Literature

Nicholas Greenspan·Yuqi Si·Kirk Roberts

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Abstract

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.

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Nicholas Greenspan, Yuqi Si, Kirk Roberts. 2020-09-30. Extracting Concepts for Precision Oncology from the Biomedical Literature. https://arxiv.org/abs/2010.00074

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