Extracting Concepts for Precision Oncology from the Biomedical Literature

TL;DR

This paper presents a dataset and NLP methods for extracting key concepts related to precision oncology from biomedical literature, aiming to improve information retrieval and analysis in this domain.

Contribution

It introduces a new annotated corpus and evaluates BERT-based models for concept extraction in precision oncology literature.

Findings

Best BERT model achieved 63.8% precision

Recall reached 71.9%, F1 score 67.1%

Proposes future research directions for system improvement

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.