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projects:workgroups:wg_meeting_02032016 [2016/03/07 21:18]
anu_gururaj created
projects:workgroups:wg_meeting_02032016 [2016/03/07 21:53]
anu_gururaj
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-====== ​Minutes_Meeting_01062016 ​======+====== ​Minutes_Meeting_02032016 ​======
  
 ==== Attendees ==== ==== Attendees ====
  
-Hua Xu, Jon Duke, George Hripcsak, Karthik Natarajan, Anupama Gururaj, Mark Khayter, Min Jiang, Don Torok, Alexandre Yahi, Andrew Williams+Hua Xu, Jon Duke, George Hripcsak, Karthik Natarajan, Anupama Gururaj, Mark Khayter, Min Jiang, Alexandre Yahi, Noemie Elhadad, Juan M Banda, Olga Patterson, Lian
  
 ==== Agenda ==== ==== Agenda ====
  
-      ​-IRB for use of clinical text +  ​Minimal Model Presentation – Alex 
-      -Clinical text data storage ​and representation schema +  Note-type mapping Presentation – Karthik 
-      -NLP tools/​pipelines ​for ETL +  - Share existing ontologies from Vanderbilt (Hua) and Regenstrief (Jon) 
-      -Use cases, e.g, phenotyping ​for cohort selection ​using NLP outputs +  Share strategies ​for combining data from different searches – Jon 
-      -Discussion+  Report on WG for commenting – Hua 
 +  - Wrappers for cTAKES and Metamap – Min 
 +  - Improvements to search engine set up using MT samples – Min 
 +  Textual Data Representation – Discussion 
 +  - Goals of 2016 
 +  - Change of meeting time
  
 ===Minutes=== ===Minutes===
  
-  - General IRB document for use of clinical text and approval from all contributors,​ post online ​Almost completed +  - Minimal model presentation - Alex 
-  Collect minimum ​set of modifiers ​for all clinical entities that support use of rule to derive clinical conceptsAlex +        - the model is based on the SHARE-N model and adapted to the current data structure. This model incorporates other semantic types and all of the modifiers are not available in cTAKES yet. 
-      * cTAKES ​is being run on clinical ​notes programmatically. Alex will present the minimal ​model in the next meeting.+        ​the notes were processed from eMERGE cohort at Columbia with about 60,000 notes encompassing 1700 patients. The original patient number was 3200. 
 +        In theory, a set containing the combination ​of minimal ​modifiers ​can be generated. Practically,​ can we trust the data enough ​to add it into OHDSI tables? - only highest confidence data (with maximum PPV) should be added to the tables. 
 +        - Next steps
 +          - Look at the note sections to determine the errors. 
 +          - Work with Sunny to generate the NLP outputs for the phenotyping data 
 +          - Evaluate by comparisons with structured data 
 +          - Make the system more robust 
 +          - Generate a protocol and/or annotation guidelines 
 +          - Share the data as a Gold standard with manually annotated CUIs 
 +          - Alex's script ​is to be tried on different datasets and evaluated across ​notes from different institutions 
 +          - Identify ​minimal ​set of notes to work with when recommending to the OHDSI community 
   - Aggregate and share note-type metadata from various sources: Karthik   - Aggregate and share note-type metadata from various sources: Karthik
       * LOINC note type mapping would be a very useful resource. We should generate hierarchical representation of note-types as an ontology. Karthink will present his work to date at the next meeting.       * LOINC note type mapping would be a very useful resource. We should generate hierarchical representation of note-types as an ontology. Karthink will present his work to date at the next meeting.
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   - Improvements to search engine set up using MT samples - Min   - Improvements to search engine set up using MT samples - Min
   - Textual Data Representation - Discussion   - Textual Data Representation - Discussion
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projects/workgroups/wg_meeting_02032016.txt · Last modified: 2016/03/09 20:31 by anu_gururaj