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From Tim Miller <timothy.mil...@childrens.harvard.edu>
Subject Re: Extracting Symptoms
Date Tue, 06 Aug 2013 16:15:12 GMT
I don't know of anyone that's done exactly what you're asking, but I 
think it's a really interesting idea. My first thought was that you 
could try the Finding typeID which would be one level less granular the 
TUIs. But that covers many more TUIs:

that contains T184, but also the noisier T033 and T047, along with many 
others! So that would make your problem worse.

Unfortunately it sounds like from what you're saying that the UMLS 
doesn't have the granularity in the places that you need to represent 
only the findings that you're interested in.

Are there any examples of the types of things that come up from T033 and 
T047 that you aren't interested in? I'm wondering if there's a pattern 
that you may be able to write rules to find so that you can 
over-generate and then filter with those rules. Just throwing out a 
simple idea.


Do you think if you moved to one level more abstract you would get too 
On 08/06/2013 11:47 AM, Bohne, Jacqueline R wrote:
> We are trying to create a cTAKES process that will extract all 
> symptoms from our documents.  In our first attempt, we used the UMLS 
> dictionary and pulled anything with a TUI of T184 (Sign or Symptom).  
> While this worked, we found that when we compared it to what our 
> Research Coordinators manually abstracted as symptoms, there were 
> quite a few differences.  When we looked into these differences we 
> found a lot of the extra terms were considered either Findings (T033) 
> or Disease or Syndrome (T047) in UMLS.  We would rather not just add 
> these TUIs to our NLP process because then we would end up with many 
> more terms than just symptoms in our results.
> Has anyone else tried to create a database of symptoms using NLP?  Or 
> are you aware of a better solution for creating a symptoms database?
> Thank you for your time!
> Thanks,
> Jacquie Bohne
> Research Programmer/Analyst
> Marshfield Clinic
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