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--- projects:workgroups:wg_meeting_01062016 [2016/01/29 22:30] – anu_gururaj
+++ projects:workgroups:wg_meeting_01062016 [2016/03/07 22:48] (current) – anu_gururaj
@@ Line 19: / Line 19: @@
       * 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. 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. 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|}}
-  - Presentation
+      * The interface being developed should present a summary with visualization for patients/notes.
-  -Updates from Annual meeting
+      * We will add Boolean query options to improve the search
-     * Extensive interest from the OHDSI community with reference to the text processing aspect. During the meeting, suggestions for improvements in the current projects were received.
+      * We will implement a Ranking algorithm
-  -IRB for use of clinical text
+      * Assign fake patient ID's to the notes to generate the visualization portion.
-    * IRB language pertaining to textual part of the record is being compiled from multiple sources.
+      * Generate a program like Circe to define the patient cohort
-    * Anu will collect and generate a generic document for use as an example.
+      * Next steps: How to move the data from textual searches stored in a table outside of OMOP to the OMOP?
-    * Once approval of the document is obtained from the contributors, the document will be posted online for use by the OHDSI community.
+          * 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
-  -Clinical text data storage and representation schema
+          * Run NLP on the ElasticSearch to extract information
-    * Minimum set of modifiers for all clinical entities that support use of rule to derive clinical concepts will be generated by Alex (Columbia).
+  - Wrappers for cTAKES and Metamap
-    * To classify the notes for the representation schema, metadata about the notes with note-type defined in detail and mapped to LOINC codes will be generated.
+  - Report on the WG - Hua will generate and share with the members for comments
-    * Note types from different institutions will be collected. George will share hierarchical note type metadata. Also, we will collect note type metadata from Josh Denny at Vanderbilt. All the collected material will be aggregated by Karthik.
+  - The best ways to represent textual data need to be determined
-  -NLP tools/pipelines for ETL
-    * The plan is to develop a set of wrappers for multiple NLP tools (currently cTAKES and MetaMap) for conversion of output to the OHDSI textual data schema.
-    * In order to get an idea of the updates in cTAKES, need to invite Guergana Savova to present and do a demo of cTAKES during the January call.
-    * In order to prioritize the work, focus on positive concepts first for high confidence extraction of NER from text.
-  -Use cases, e.g, phenotyping for cohort selection using NLP outputs
-    * To define the syntax for storing phenotypes, two aspects can be considered:
-           - set of data elements or features on which an algorithm functions
-           - formulation of the phenotype definition
-    * In order to represent the NLP output, query-based phenotyping will be the first focus of the group.
-    * For machine-learning based algorithms, the NLP output will be accessed outside of the CDM
-    * Is ElasticSearch a good first step in this area? ES should be considered here as a tool more for cohort building and selection rather than phenotyping. For this purpose, it is a good starting point.
-    * Finding patients for clinical trials will be used as a usecase here. The ES could serve as an explorer for feature selection in the phenotyping process.
-    * Action item: Simple search set up for MT samples by next meeting by Min.
-    * Use MIMICII and MIMICIII as demo  datasets for the tools being developed by the group
-  -Discussion
 ===Action Items===
-  -
+  - Minimal Model Presentation - Alex
-  - IRB for use of clinical text
+  - Note-type mapping Presentation - Karthink
-  - Clinical text data storage and representation schema
+  - Share existing ontologies from Vanderbilt (Hua) and Regenstrief (Jon)
-  - NLP tools/pipelines for ETL
+  - Share strategies for combining data from different searches - Jon
-  - Use cases, e.g, phenotyping for cohort selection using NLP outputs
+  - Report on WG for commenting - Hua
-  - Discussion
+  - Wrappers for cTAKES and Metamap - Min
+  - Improvements to search engine set up using MT samples - Min
+  - Textual Data Representation - Discussion