I’ve learned a lot listening in on the working group calls but have a fundamental question:
I cannot, for the life of me, work out how to get the CLAMP wrapper configured and running.
I’m labeling a messy problem history data set… I think I would like to use Usagi to identify consistent concepts associated with my headers but will want to use CLAMP to pick out concepts associated with the free text in the subsequent “explain” parts.
My tentative development plan was going to be to have human annotators mark up a gold-standard set of our problem lists in CLAMP and then use its machine learning tooling to refine the stock OHDSI pipeline. Will this approach work?
I’m also very interested in practical ways to use the NLP objects you’ve designed. My first instinct was going to be when I discovered (for example) a specific medication, that I would write a row to the DRUG_EXPOSURE table. But am I jumping too far from the intent of the NLP work?
These may be remedial questions - but I’d be grateful for a little help coming up to speed with ways to operationalize this tooling.
UM School of Dentistry