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We present RadGraph2, a novel dataset for extracting information fromradiology reports that focuses on capturing changes in disease state and deviceplacement over time. We introduce a hierarchical schema that organizes entitiesbased on their relationships and show that using this hierarchy during trainingimproves the performance of an information extraction model. Specifically, wepropose a modification to the DyGIE++ framework, resulting in our model HGIE,which outperforms previous models in entity and relation extraction tasks. Wedemonstrate that RadGraph2 enables models to capture a wider variety offindings and perform better at relation extraction compared to those trained onthe original RadGraph dataset. Our work provides the foundation for developingautomated systems that can track disease progression over time and developinformation extraction models that leverage the natural hierarchy of labels inthe medical domain.

RadGraph2: Modeling Disease Progression in Radiology Reports via Hierarchical Information Extraction