Summary of Llmd: a Large Language Model For Interpreting Longitudinal Medical Records, by Robert Porter et al.
LLMD: A Large Language Model for Interpreting Longitudinal Medical Records
by Robert Porter, Adam Diehl, Benjamin Pastel, J. Henry Hinnefeld, Lawson Nerenberg, Pye Maung, Sebastien Kerbrat, Gillian Hanson, Troy Astorino, Stephen J. Tarsa
First submitted to arxiv on: 11 Oct 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 LLMD, a large language model that analyzes medical histories based on patient records. By combining domain knowledge with training on vast amounts of records and labels, LLMD makes nuanced connections to provide an accurate picture of patient health. This approach surpasses models relying solely on knowledge, unlabeled data, structured EHRs, or single-system records. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMD is a big computer program that helps doctors understand patients’ medical history by looking at many old records from different places. It’s special because it combines what we know about medicine with lots of information from those records and labels to make smart connections. This makes it better than other programs that just use one type of data or don’t have any labels. |
Keywords
» Artificial intelligence » Large language model
