Summary of Representation Learning Of Structured Data For Medical Foundation Models, by Vijay Prakash Dwivedi et al.
Representation Learning of Structured Data for Medical Foundation Models
by Vijay Prakash Dwivedi, Viktor Schlegel, Andy T. Liu, Thanh-Tung Nguyen, Abhinav Ramesh Kashyap, Jeng Wei, Wei-Hsian Yin, Stefan Winkler, Robby T. Tan
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 examines the limitations of Large Language Models (LLMs) in processing medical codes, specifically those used in records like ICD-10 or SNOMED-CT. Current tokenization methods are insufficient, leading to challenges in representing structured non-textual data. To address this, the authors introduce the UniStruct architecture for a multimodal medical foundation model that combines unstructured text and structured data. The approach adapts subword tokenization techniques specifically for medical codes and is validated through pre-training on internal and public medical databases. The proposed model achieves up to 23% improvement in evaluation metrics, with a 2% gain attributed to the new tokenization method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps Large Language Models better understand structured medical data like ICD-10 or SNOMED-CT codes. Current models are not good at handling this type of information, which is important for healthcare. The authors create a new way to represent this information by combining unstructured text and structured data. They train their model on lots of medical data and show it can improve performance on many tasks. |
Keywords
» Artificial intelligence » Tokenization
