CohortFlow: steerable agent for clinical cohort curation
The system is capable of making localized updates to a structured cohort specification and reconciling these changes with a persistent execution graph. This allows it to reduce correction latency by up to 64.8% compared to full regeneration using the same model, as demonstrated on 100 scenarios derived from clinical trial eligibility criteria. CohortFlow also shows promise in reducing the number…
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Key points
- CohortFlow reduces correction latency by up to 64.8%
- Localized steering achieves a micro-F1 score of 77.0%
- It maintains strong requirement-level accuracy in MIMIC evaluations
Read the original at arXiv cs.AI · by Neil Chen, Adrian Serapio, Hanxue Gu, Evan Lee, Kang Wang, Yang Yang primary sourceOpen source ↗
The headline, key points and digest above were generated by Digest AI's editorial model from the linked sources. Automated summaries can contain errors: the sources are the record. Spotted a mistake? Tell us. Published by Martin K., who runs Digest AI and handles corrections.
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