Summary of Introducing the Large Medical Model: State Of the Art Healthcare Cost and Risk Prediction with Transformers Trained on Patient Event Sequences, by Ricky Sahu et al.
Introducing the Large Medical Model: State of the art healthcare cost and risk prediction with transformers trained on patient event sequences
by Ricky Sahu, Eric Marriott, Ethan Siegel, David Wagner, Flore Uzan, Troy Yang, Asim Javed
First submitted to arxiv on: 19 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Applications (stat.AP); Machine Learning (stat.ML)
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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 paper introduces the Large Medical Model (LMM), a generative pre-trained transformer designed to guide patient care and healthcare administration. Trained on 140M longitudinal patient claims records, the model demonstrates superior capabilities in forecasting healthcare costs, identifying risk factors, and detecting intricate patterns within complex medical conditions. The LMM improves cost prediction by 14.1% over commercial models and chronic conditions prediction by 1.9% over transformer models, offering potential to enhance risk assessment, cost management, and personalized medicine. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better predict patient care and healthcare costs by creating a special model called the Large Medical Model (LMM). The LMM uses big data from many patients and can find patterns that help doctors make better decisions. It’s like having a super-smart doctor who can look at lots of information to figure out what’s best for each patient. |
Keywords
» Artificial intelligence » Transformer
