Summary of Large Language Models For Medical Forecasting — Foresight 2, by Zeljko Kraljevic et al.
Large Language Models for Medical Forecasting – Foresight 2
by Zeljko Kraljevic, Joshua Au Yeung, Daniel Bean, James Teo, Richard J. Dobson
First submitted to arxiv on: 14 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 The Foresight 2 (FS2) large language model is specifically designed for biomedical applications, particularly in hospital settings. By fine-tuning the model on hospital data, it can understand clinical notes and predict SNOMED codes for various tasks such as diagnosis suggestions, risk forecasting, and procedure recommendations. The FS2 model shows significant improvements over the state-of-the-art in predicting new biomedical concepts and disorders, outperforming larger models like GPT-4-turbo when fine-tuned on high-quality hospital data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Foresight 2 is a special kind of computer program that helps doctors make better decisions. It looks at notes from hospitals to figure out what’s going on with patients. The new model does this job really well, better than older models. It can even predict what might happen next in a patient’s health, like if they’ll get sick or need more treatment. This shows that using special hospital data makes the computer program smarter. |
Keywords
» Artificial intelligence » Fine tuning » Gpt » Large language model
