Return-Path: X-Original-To: apmail-ctakes-user-archive@www.apache.org Delivered-To: apmail-ctakes-user-archive@www.apache.org Received: from mail.apache.org (hermes.apache.org [140.211.11.3]) by minotaur.apache.org (Postfix) with SMTP id 92BED10827 for ; Tue, 6 Aug 2013 15:48:04 +0000 (UTC) Received: (qmail 45426 invoked by uid 500); 6 Aug 2013 15:48:04 -0000 Delivered-To: apmail-ctakes-user-archive@ctakes.apache.org Received: (qmail 45220 invoked by uid 500); 6 Aug 2013 15:48:00 -0000 Mailing-List: contact user-help@ctakes.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Reply-To: user@ctakes.apache.org Delivered-To: mailing list user@ctakes.apache.org Received: (qmail 45206 invoked by uid 99); 6 Aug 2013 15:47:58 -0000 Received: from nike.apache.org (HELO nike.apache.org) (192.87.106.230) by apache.org (qpsmtpd/0.29) with ESMTP; Tue, 06 Aug 2013 15:47:58 +0000 X-ASF-Spam-Status: No, hits=2.2 required=5.0 tests=HTML_MESSAGE,SPF_PASS X-Spam-Check-By: apache.org Received-SPF: pass (nike.apache.org: domain of bohne.jacqueline@mcrf.mfldclin.edu designates 192.236.17.118 as permitted sender) Received: from [192.236.17.118] (HELO mailhost3.marshfieldclinic.org) (192.236.17.118) by apache.org (qpsmtpd/0.29) with ESMTP; Tue, 06 Aug 2013 15:47:49 +0000 Received: from pps.filterd (mailhost3.mfldclin.edu [127.0.0.1]) by mailhost3.mfldclin.edu (8.14.5/8.14.5) with SMTP id r76FhVxN009854 for ; Tue, 6 Aug 2013 10:47:27 -0500 Received: from mcl-exhub03.mfldclin.org (MCL-EXHUB03.mfldclin.org [10.1.2.112]) by mailhost3.mfldclin.edu with ESMTP id 1e1mnwvbjk-1 for ; Tue, 06 Aug 2013 10:47:27 -0500 Received: from MCL-EXMB02.mfldclin.org ([169.254.2.45]) by MCL-EXHUB03.mfldclin.org ([::1]) with mapi id 14.02.0309.002; Tue, 6 Aug 2013 10:47:27 -0500 From: "Bohne, Jacqueline R" To: "user@ctakes.apache.org" Subject: Extracting Symptoms Thread-Topic: Extracting Symptoms Thread-Index: Ac6SuAPd8bhgTRzHQIOZ7LvEMHtJJA== Date: Tue, 6 Aug 2013 15:47:27 +0000 Message-ID: <739EEBD7287586409FB46BBDFEE89B624BCD61CD@MCL-EXMB02.mfldclin.org> Accept-Language: en-US Content-Language: en-US X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [10.1.2.5] Content-Type: multipart/alternative; boundary="_000_739EEBD7287586409FB46BBDFEE89B624BCD61CDMCLEXMB02mfldcl_" MIME-Version: 1.0 X-Proofpoint-Virus-Version: vendor=fsecure engine=2.50.10432:5.10.8794,1.0.431,0.0.0000 definitions=2013-08-05_07:2013-08-05,2013-08-05,1970-01-01 signatures=0 X-Virus-Checked: Checked by ClamAV on apache.org --_000_739EEBD7287586409FB46BBDFEE89B624BCD61CDMCLEXMB02mfldcl_ Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable We are trying to create a cTAKES process that will extract all symptoms fro= m our documents. In our first attempt, we used the UMLS dictionary and pul= led anything with a TUI of T184 (Sign or Symptom). While this worked, we f= ound that when we compared it to what our Research Coordinators manually ab= stracted as symptoms, there were quite a few differences. When we looked i= nto these differences we found a lot of the extra terms were considered eit= her 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 wi= th many more terms than just symptoms in our results. Has anyone else tried to create a database of symptoms using NLP? Or are y= ou aware of a better solution for creating a symptoms database? Thank you for your time! Thanks, Jacquie Bohne Research Programmer/Analyst Marshfield Clinic ______________________________________________________________________ The contents of this message may contain private, protected and/or privileg= ed information. If you received this message in error, you should destroy = the e-mail message and any attachments or copies, and you are prohibited fr= om retaining, distributing, disclosing or using any information contained w= ithin. Please contact the sender and advise of the erroneous delivery by r= eturn e-mail or telephone. Thank you for your cooperation. --_000_739EEBD7287586409FB46BBDFEE89B624BCD61CDMCLEXMB02mfldcl_ Content-Type: text/html; charset="us-ascii" Content-Transfer-Encoding: quoted-printable

We are trying to create a cTAKES process that will e= xtract all symptoms from our documents.  In our first attempt, we used= the UMLS dictionary and pulled anything with a TUI of T184 (Sign or Sympto= m).  While this worked, we found that when we compared it to what our Research Coordinators manually abstracted as sy= mptoms, there were quite a few differences.  When we looked into these= differences we found a lot of the extra terms were considered either Findi= ngs (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 sympto= ms using NLP?  Or are you aware of a better solution for creating a sy= mptoms database?

 

Thank you for your time!

 

Thanks,

Jacquie Bohne

Research Programmer/Analyst

Marshfield Clinic

The contents of this message may contain private, protected and/or priv= ileged information. If you received this message in error, you should dest= roy the e-mail message and any attachments or copies, and you are prohibite= d from retaining, distributing, disclosing or using any information contain= ed within. Please contact the sender and advise of the erroneous delivery = by return e-mail or telephone. Thank you for your cooperation.
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