RadGraph: Extracting Clinical Entities and Relations from Radiology Reports

RadGraph is a dataset of entities and relations in full-text radiology reports. We designed a novel information extraction (IE) schema to structure clinical information in a radiology report with four entities and three relations. Our train set consists of 500 MIMIC-CXR radiology reports annotated according to our schema by board-certified radiologists. Our test set consists of 50 MIMIC-CXR and 50 CheXpert reports, which are independently annotated by two board-certified radiologists. Additionally, we release annotations generated by a benchmark deep learning model that achieves a micro F1 of 0.82 (MIMIC-CXR test set) and 0.73 (CheXpert test set) on an evaluation metric for end-to-end relation extraction, where entity boundaries, entity types, and relation type must be correct. We use our model to automatically generate entity and relation labels across 220,763 MIMIC-CXR  reports and 500 CheXpert reports, where annotations can be mapped to associated chest radiographs in the MIMIC-CXR and CheXpert datasets respectively. The dataset, which includes reports, entities, and relations, is de-identified according to the US Health Insurance Portability Act (HIPAA). This dataset is intended to support the development of natural language processing (NLP) methods for entity and relation extraction in radiology as well as enable multi-modal use cases that can leverage entities, relations, and associated radiographs.

RadGraph 是一份包含全文放射学报告中的实体与关系的数据库。本团队设计了一套新颖的信息提取（IE）方案，用以在放射学报告中以四个实体和三个关系的形式结构化临床信息。我们的训练集由500份按照该方案由执业资格认证的放射科医生标注的MIMIC-CXR放射学报告组成。测试集则由50份MIMIC-CXR和50份CheXpert报告构成，这些报告由两位执业资格认证的放射科医生独立标注。此外，我们还发布了由基准深度学习模型生成的标注，该模型在端到端关系提取的评估指标上分别取得了0.82（MIMIC-CXR测试集）和0.73（CheXpert测试集）的微F1分数。我们运用该模型自动对220,763份MIMIC-CXR报告和500份CheXpert报告中的实体与关系标签进行生成，其中标注可与MIMIC-CXR和CheXpert数据集中的相关胸部X光片相对应。该数据集，包含报告、实体与关系，根据美国健康保险可携带性和责任法案（HIPAA）进行了去标识化处理。本数据集旨在支持自然语言处理（NLP）方法在放射学领域中的实体与关系提取技术的发展，并促进能够利用实体、关系及其相关放射学图像的多模态应用场景的开发。

搜集汇总

背景与挑战

背景概述

RadGraph是一个专注于从放射学报告中提取临床实体和关系的数据集，包含高质量的人工标注（训练集500份MIMIC-CXR报告，测试集100份跨机构报告）和自动生成的扩展标注（覆盖22万余份MIMIC-CXR和500份CheXpert报告）。该数据集设计了一个新颖的信息提取模式，包含四种实体和三种关系，旨在支持自然语言处理方法和多模态应用开发，例如结合放射学报告和关联的胸片图像进行分析。