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RadGraph2: A New Dataset for Tracking Disease Progression in Radiology Reports

RadGraph2: A New Dataset for Tracking Disease Progression in Radiology Reports

Practical AI Solutions for Automated Information Extraction from Radiology Reports

Challenges in Medical Informatics

Extracting and interpreting complex medical data from radiology reports, particularly tracking disease progression over time, poses significant challenges due to limited labeled data availability.

RadGraph2: Enhanced Schema and Model

RadGraph2 introduces an enhanced hierarchical schema, RadGraph2, and employs a Hierarchical Graph Information Extraction (HGIE) model to automatically annotate radiology reports.

Main Entity and Relation Types

The schema defines three main entity types: “anatomy,” “observation,” and “change,” along with modify, located_at, and suggestive_of relations to capture complex interconnections between entities in radiology reports.

Structured Dataset and File Format

The dataset is organized into training, development, and test sets, along with over 220,000 automatically labeled reports, and is provided in a JSON format with detailed metadata and entity information for each report.

Value and Practical Application

RadGraph2 enables more comprehensive representation of temporal changes in radiology reports, offering a robust framework for developing advanced natural language processing models in the medical domain.

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I believe that AI is only as powerful as the human insight guiding it.

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