# Vivax

- **Event:** [Built with Opus 4.7: a Claude Code hackathon](https://cerebralvalley.ai/e/built-with-4-7-hackathon)
- **When:** Apr 21 at 12:00 PM – Apr 27 at 2:00 AM (EDT)
- **Where:** Online
- **Team:** [Umut Kilinckaya](https://cerebralvalley.ai/u/Umut-K)
- **GitHub:** https://github.com/Umut-K/GraphDB_for_clinical_records
- **Demo video:** https://youtu.be/cuB3ykZn_nE
- **Gallery:** https://cerebralvalley.ai/e/built-with-4-7-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-4-7-hackathon/hackathon/gallery/52

Clinical research leads spend weeks manually identifying care gaps and overprescription patterns in their patient population — querying EHRs, cross-referencing guidelines, validating statistically, packaging interventions. The Clinical Hypothesis Scout collapses this loop. A 7-phase Claude Opus 4.7 agent runs over a Neo4j graph of 21,582 patients, four medical ontologies (SNOMED, RxNorm, ATC, LOINC), and 458 recommendations extracted from 64 American clinical guidelines. Each surfaced hypothesis must pass three statistical guardrails, survive a self-critique that auto-declines synthetic-data artifacts, and arrive packaged with five live PubMed citations and an operational action plan covering intervention, owner, complexity, and dollar impact.                                         

What separates it from a hypothesis generator is the continuous-monitoring layer. The scout fingerprints each clinical pattern and emits NEW alerts when a pattern first appears, STRENGTHENED when its cohort grows by 20% or more across batches — in the demo dataset, a bisphosphonate-overprescription signal grew from 303 to 450 to 587 patients across three consecutive batches, the kind of consolidating pattern a research lead would want paged about. A Next.js dashboard exposes kept and declined queues with reasons, a cohort browser, a streaming NL query interface, and reproducible Cypher for every finding.

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