Fail Fast, Save Millions: AI for Smarter Clinical Trials
summary
Sii presents an early‑warning system that analyzes startup‑phase clinical trial protocol documents using classical machine‑learning ensembles combined with four specialized BERT models and novel text features. The hybrid classifier achieves about 73% accuracy on the public ClinicalTrials dataset, gains roughly an 8% improvement from text embeddings, runs as a GPU‑free laptop prototype, and back‑testing across therapeutic areas suggests it can flag high‑risk trials and potentially save about $125M in portfolio costs.
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chapters (7)
- 0:00Introduction
- 3:38Clinical trials overview
- 10:22Planning phase data
- 17:35Hierarchical predictive model
- 26:55Model results literature
- 29:42Summary next steps
- 30:46Q&A
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