RadGraph2: Modeling Disease Progression in Radiology Reports via
  Hierarchical Information Extraction
Sameer Khanna Adam Dejl Kibo Yoon Quoc Hung Truong Hanh Duong Agustina Saenz Pranav Rajpurkar
We present RadGraph2, a novel dataset for extracting information from
radiology reports that focuses on capturing changes in disease state and device
placement over time. We introduce a hierarchical schema that organizes entities
based on their relationships and show that using this hierarchy during training
improves the performance of an information extraction model. Specifically, we
propose a modification to the DyGIE++ framework, resulting in our model HGIE,
which outperforms previous models in entity and relation extraction tasks. We
demonstrate that RadGraph2 enables models to capture a wider variety of
findings and perform better at relation extraction compared to those trained on
the original RadGraph dataset. Our work provides the foundation for developing
automated systems that can track disease progression over time and develop
information extraction models that leverage the natural hierarchy of labels in
the medical domain.