Summary of High-throughput Phenotyping Of Clinical Text Using Large Language Models, by Daniel B. Hier et al.
High-Throughput Phenotyping of Clinical Text Using Large Language Models
by Daniel B. Hier, S. Ilyas Munzir, Anne Stahlfeld, Tayo Obafemi-Ajayi, Michael D. Carrithers
First submitted to arxiv on: 2 Aug 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 A novel approach to precision medicine is proposed, leveraging large language models to automate high-throughput phenotyping. By mapping patient signs to standardized ontology concepts, this study demonstrates the effectiveness of GPT-4 in identifying, categorizing, and normalizing clinical summaries from the OMIM database. Compared to GPT-3.5-Turbo, GPT-4 achieves comparable concordance with manual annotators, showcasing its generalizability across various phenotyping tasks while eliminating the need for manually annotated training data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a way to quickly and accurately identify important health information from patient records using special computer models. These models can read through lots of medical notes and find specific details that are important for doctors to know. In this study, they compared two different models, GPT-4 and GPT-3.5-Turbo, to see which one did a better job of finding and organizing the important health information. The results show that GPT-4 is really good at doing this and can even do it without needing special training data. This could be a big help for doctors who need to quickly understand large amounts of patient information. |
Keywords
» Artificial intelligence » Gpt » Precision
