  Extracting structured clinical information from free-text radiology reports
can enable the use of radiology report information for a variety of critical
healthcare applications. In our work, we present RadGraph, a dataset of
entities and relations in full-text chest X-ray radiology reports based on a
novel information extraction schema we designed to structure radiology reports.
We release a development dataset, which contains board-certified radiologist
annotations for 500 radiology reports from the MIMIC-CXR dataset (14,579
entities and 10,889 relations), and a test dataset, which contains two
independent sets of board-certified radiologist annotations for 100 radiology
reports split equally across the MIMIC-CXR and CheXpert datasets. Using these
datasets, we train and test a deep learning model, RadGraph Benchmark, that
achieves a micro F1 of 0.82 and 0.73 on relation extraction on the MIMIC-CXR
and CheXpert test sets respectively. Additionally, we release an inference
dataset, which contains annotations automatically generated by RadGraph
Benchmark across 220,763 MIMIC-CXR reports (around 6 million entities and 4
million relations) and 500 CheXpert reports (13,783 entities and 9,908
relations) with mappings to associated chest radiographs. Our freely available
dataset can facilitate a wide range of research in medical natural language
processing, as well as computer vision and multi-modal learning when linked to
chest radiographs.
