Summary of Radex: a Framework For Structured Information Extraction From Radiology Reports Based on Large Language Models, by Daniel Reichenpfader et al.
RadEx: A Framework for Structured Information Extraction from Radiology Reports based on Large Language Models
by Daniel Reichenpfader, Jonas Knupp, André Sander, Kerstin Denecke
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces RadEx, an end-to-end framework for developing automated information extraction systems from radiology reports. The framework consists of 15 software components and ten artifacts that enable the creation of structured reports. The RadEx framework allows clinicians to define relevant information for specific clinical domains and create report templates. It also supports both generative and encoder-only models, enabling independent model improvements. The decoupling of information extraction from template filling facilitates implementation and maintenance as components are easily exchangeable, while standardized artifacts ensure interoperability between components. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates a tool called RadEx to help make radiology reports more useful by making them easier to understand. Radiologists write these reports in a way that’s hard for computers to read, but RadEx helps to change this. It lets doctors define what information is important and create templates for their reports. This makes it easier to get the information needed from the reports, which can be used for things like predicting health outcomes. |
Keywords
» Artificial intelligence » Encoder
