Summary of Novel Development Of Llm Driven Mcode Data Model For Improved Clinical Trial Matching to Enable Standardization and Interoperability in Oncology Research, by Aarsh Shekhar and Mincheol Kim
Novel Development of LLM Driven mCODE Data Model for Improved Clinical Trial Matching to Enable Standardization and Interoperability in Oncology Research
by Aarsh Shekhar, Mincheol Kim
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This novel framework streamlines data standardization, interoperability, and exchange in oncology by leveraging advanced language models (LLMs) and computer engineering. The approach enhances integration across disparate healthcare systems, facilitating timely and accurate sharing of patient information. By transforming unstructured patient treatment data into enriched mCODE profiles, the system facilitates seamless integration with an AI-based clinical trial matching engine. Results show significant improvement in data standardization, with accuracy rates peaking at 92%. The LLM also demonstrated high accuracy rates for SNOMED-CT (90%), LOINC (87%), and RxNorm codes (84%). This surpasses current averages of 77% achieved by LLMs like GPT-4 and Claude’s 3.5. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors and researchers work better together to diagnose and treat cancer. They use special computer programs to make sure patient information is correct and shared correctly between different hospitals and clinics. This makes it easier to find the right treatment for patients, which can be a big help in improving cancer care. The system also looks at unstructured data like PDFs and notes to get more accurate results. |
Keywords
» Artificial intelligence » Claude » Gpt
