๐ฌ Training Transformers to solve 95% failure rate of Cancer Trials โ Ron Alfa & Daniel Bear, Noetik
๐ฐ Latent Space
Learn how Noetik uses autoregressive transformers like TARIO-2 to tackle the 95% failure rate of cancer trials by solving a matching problem
Action Steps
- Apply autoregressive transformers to clinical trial data
- Configure TARIO-2 model for cancer treatment matching
- Test the performance of the model on historical trial data
- Compare the results with traditional matching methods
- Refine the model based on the comparison results
Who Needs to Know This
Data scientists and AI engineers on a cancer research team can benefit from this approach to improve clinical trial success rates
Key Insight
๐ก Autoregressive transformers can be used to improve cancer trial success rates by solving matching problems
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๐ฌ Noetik's TARIO-2 autoregressive transformer tackles 95% cancer trial failure rate by solving matching problems! #AIforCancerResearch
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