Hua Xu, Jon Duke, George Hripcsak, Karthik Natarajan, Anupama Gururaj, Mark Khayter, Min Jiang, Alexandre Yahi, Noemie Elhadad, Juan M Banda, Olga Patterson, Lian

1. Minimal Model Presentation – Alex
2. Note-type mapping Presentation – Karthik
3. Share existing ontologies from Vanderbilt (Hua) and Regenstrief (Jon)
4. Share strategies for combining data from different searches – Jon
5. Report on WG for commenting – Hua
6. Wrappers for cTAKES and Metamap – Min
7. Improvements to search engine set up using MT samples – Min
8. Textual Data Representation – Discussion
9. Goals of 2016
10. Change of meeting time

1. Minimal model presentation - Alex
   1. 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.
   2. the notes were processed from eMERGE cohort at Columbia with about 60,000 notes encompassing 1700 patients. The original patient number was 3200.
2. Collect minimum set of modifiers for all clinical entities that support use of rule to derive clinical concepts: Alex
   • cTAKES is being run on clinical notes programmatically. Alex will present the minimal model in the next meeting.
3. 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.
   • Existing ontology for note types to be shared : Vanderbilt (Hua) and Regenstrief (Jon)
4. Simple search set up for MT samples: MinPresentation
   • Presentation
   • The interface being developed should present a summary with visualization for patients/notes.
   • We will add Boolean query options to improve the search
   • We will implement a Ranking algorithm
   • Assign fake patient ID's to the notes to generate the visualization portion.
   • Generate a program like Circe to define the patient cohort
   • Next steps: How to move the data from textual searches stored in a table outside of OMOP to the OMOP?
     • Structured searches from CDW and textual searches can be combined using existing strategies. Jon will share the slides of his presentation on combining data from different searches
     • Run NLP on the ElasticSearch to extract information
5. Wrappers for cTAKES and Metamap
6. Report on the WG - Hua will generate and share with the members for comments
7. The best ways to represent textual data need to be determined

1. Minimal Model Presentation - Alex
2. Note-type mapping Presentation - Karthink
3. Share existing ontologies from Vanderbilt (Hua) and Regenstrief (Jon)
4. Share strategies for combining data from different searches - Jon
5. Report on WG for commenting - Hua
6. Wrappers for cTAKES and Metamap - Min
7. Improvements to search engine set up using MT samples - Min
8. Textual Data Representation - Discussion