Observational Health Data Sciences and Informatics

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====== Minutes_Meeting_01062016 ======

==== Attendees ====

Hua Xu, Jon Duke, George Hripcsak, Karthik Natarajan, Anupama Gururaj, Mark Khayter, Min Jiang, Don Torok, Alexandre Yahi, Andrew Williams

==== Agenda ====

      -IRB for use of clinical text
      -Clinical text data storage and representation schema
      -NLP tools/pipelines for ETL
      -Use cases, e.g, phenotyping for cohort selection using NLP outputs
      -Discussion

===Minutes===

  - General IRB document for use of clinical text and approval from all contributors, post online - Almost completed
  - 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.
  - 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. Karthik will present his work to date at the next meeting.
      * Existing ontology for note types to be shared : Vanderbilt (Hua) and Regenstrief (Jon)
  - Simple search set up for MT samples: MinPresentation
      * Presentation {{:projects:workgroups:clinical_text_search_engine_01_06.pdf|}}
      * 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
  - Wrappers for cTAKES and Metamap 
  - Report on the WG - Hua will generate and share with the members for comments
  - The best ways to represent textual data need to be determined

===Action Items===

  - Minimal Model Presentation - Alex
  - Note-type mapping Presentation - Karthink
  - Share existing ontologies from Vanderbilt (Hua) and Regenstrief (Jon)
  - Share strategies for combining data from different searches - Jon
  - 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
Observational Health Data Sciences and Informatics

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