Fine-grained spatial information extraction in radiology as two-turn question answering.

ABSTRACT:

Objectives

Radiology reports contain important clinical information that can be used to automatically construct fine-grained labels for applications requiring deep phenotyping. We propose a two-turn question answering (QA) method based on a transformer language model, BERT, for extracting detailed spatial information from radiology reports. We aim to demonstrate the advantage that a multi-turn QA framework provides over sequence-based methods for extracting fine-grained information.

Methods

Our proposed method identifies spatial and descriptor information by answering queries given a radiology report text. We frame the extraction problem such that all the main radiology entities (e.g., finding, device, anatomy) and the spatial trigger terms (denoting the presence of a spatia

SUBMITTER: Datta S 

PROVIDER: S-EPMC9072592 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature