Hua Xu, Jon Duke, George Hripcsak, Karthik Natarajan, Anupama Gururaj, Min Jiang, Alexandre Yahi, Noemie Elhadad, Juan M Banda, Olga Patterson, Scott DuVall, Vojtech Huser

nlp_wg_meeting_03092016.pdf

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

1. Note-type mapping Presentation – Karthik
   1. INYP terminology services provides clinical document browser based on LOINC ontology for clinical notes. The paper describing the system is published and the pdf is attached. loinc_note_mapping_paper.pdf
   2. the system allows for comparison of configs across different sites.
   3. Vojtech also has looked into using LOINC ontology for note types. loinc_note_types_marshall_clinic.pdf
   4. With reference to standard note types, one of the suggestions was to use it in the database for the output of the NLP system.
   5. The note table in the OMOP CDM has a note type field with about 10 or so note types
   7. Next steps:
      1. Comparison of what aspects of LOINC is being used across different institutions.
      2. One of the deliverable is to build a system that can map the institutional note type to the LOINC note types.
      3. Update the note type field of the note table in OMOP CDM. Should a subset of LOINC document types be used for this purpose? this would depend on the real-world data
2. existing ontologies from Vanderbilt - Hua - document-types.zip

1. Suggestion for OMOP model regarding NLP output - Hua/Noemie
2. Share existing ontologies from Regenstrief (Jon)
3. Share strategies for combining data from different searches - Jon
4. Report on WG for commenting - Hua
5. Wrappers for cTAKES and Metamap - Min
6. Improvements to search engine set up using MT samples - Min
7. Textual Data Representation - Discussion
8. NLP system evaluation across different sites - Discussion