Summary of Structured Extraction Of Real World Medical Knowledge Using Llms For Summarization and Search, by Edward Kim et al.
Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search
by Edward Kim, Manil Shrestha, Richard Foty, Tom DeLay, Vicki Seyfert-Margolis
First submitted to arxiv on: 16 Dec 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 A novel approach is proposed for accelerating disease discovery and analysis in real-world data by creating patient knowledge graphs using large language model extraction techniques. This methodology allows for data extraction via natural language rather than rigid ontological hierarchies, which can capture patient condition nuances or rare diseases not accounted for in codified categories like SNOMED-CT, ICD10, CPT. The proposed method maps to existing ontologies (MeSH, SNOMED-CT, RxNORM, HPO) to ground extracted entities, potentially resolving issues with ontology mapping and disease clustering. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers is working on a new way to help doctors find information about diseases in medical records. They’re using computers to look at lots of text and figure out what the important words mean. This will make it easier for doctors to understand patient conditions that don’t fit into specific categories, like rare diseases. The method they’re developing can match their findings to existing systems that doctors already use. |
Keywords
» Artificial intelligence » Clustering » Large language model
