Fallback to Ad Hoc Phenotype Creation Given Poor/No Recommendations
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 12
- Forks
- 7
- Avg merge
- 2m
- Merged PRs (30d)
- 12
Description
@rkboyce et al - this is amazing work. We chatted on the phenotype workgroup call a few weeks ago, and I'd be very interested in contributing some work I'll present at OHDSI global this fall on CAPR-based iterative phenotype development using LLMs. I'm not sure if that fits within your vision for the StudyAgent, but after some initial testing with this tool I do see scenarios where it may be advantageous to develop fresh phenotypes on the fly when there are no suitable definitions available in the index/catalog. Please let me know if you'd like to setup a brief call to discuss further.
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No files, tests, or entry points are identified. Start by reviewing how StudyAgent handles phenotype recommendations and its index/catalog, then clarify the desired CAPR-based iterative phenotype workflow and what should happen when no suitable definition is available; done should include an agreed fallback scope and validation criteria.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- ai, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Active
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100