Summary of Zero Shot Health Trajectory Prediction Using Transformer, by Pawel Renc et al.
Zero Shot Health Trajectory Prediction Using Transformer
by Pawel Renc, Yugang Jia, Anthony E. Samir, Jaroslaw Was, Quanzheng Li, David W. Bates, Arkadiusz Sitek
First submitted to arxiv on: 30 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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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 application of the transformer deep-learning architecture, called Enhanced Transformer for Health Outcome Simulation (ETHOS), is introduced for analyzing high-dimensional, heterogeneous, and episodic health data. ETHOS uses Patient Health Timelines (PHTs) to predict future health trajectories through a zero-shot learning approach. This foundation model eliminates the need for labeled data and fine-tuning, simulating various treatment pathways while considering patient-specific factors. As a tool for care optimization and addressing biases in healthcare delivery, ETHOS has significant potential. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists create a new way to use artificial intelligence (AI) to make better decisions about people’s health. They develop a system called ETHOS that can look at lots of different information about someone’s health over time and predict what might happen in the future. This helps doctors and other healthcare workers make better choices about how to treat patients and gives them more information to work with. |
Keywords
» Artificial intelligence » Deep learning » Fine tuning » Optimization » Transformer » Zero shot
