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projects:workgroups:wg_meeting_11042015 [2015/11/30 16:36] anu_gururaj |
projects:workgroups:wg_meeting_11042015 [2015/11/30 16:57] anu_gururaj |
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* 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. | * 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 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 | -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 | -Discussion |