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.

本文介绍了RadGraph数据集，这是一个基于新的信息提取模式设计的全文胸部X射线放射学报告中实体与关系的数据集。作者还发布了与之相关的开发、测试和推理数据集，并训练了一个深度学习模型——RadGraph Benchmark，该模型在MIMIC-CXR和CheXpert测试集上分别达到0.82和0.73的微F1得分。这些数据集可用于医疗自然语言处理、计算机视觉和多模态学习等领域的研究。

从影像学报告中提取临床实体和关系的RadGraph