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Smart Mapper module produces mapping specification & SDTM datasets . The input to this module is raw metadata or raw datasets from the EDC. The raw data/metadata collected in the CRF is compared against the SDTM/sponsor defined metadata. The right match will be predicted using Machine Learning. The objective is to predict about 70% of the SDTM variables and have the system trained with the selection from the user for the remaining 30% of the variables. The more studies are used for SDTM mapping in the tool, it’s better for the System to get trained with the model. Once mapping specifications is finalized, the SDTM datasets is generated as per the specification. The system will read the specification and construct programming code. There will be a single standalone program for each SDTM domain based on the mapping specification. JAVA technology is used to create the code.

Benefits:
  • Convert studies from raw data model to target SDTM
  • Auto generates mapping specification for raw data to SDTM Transformation
  • Provide input to SAS Program for Regeneration of dataset for target model
  • Integrated ML/NLP solution for keyword detection and prediction
EDC Systems Smart aCRF Raw Metadata Extraction Annotation Repository Mapping Process ML/NLP Mapping Spec Generation Programming SDTM Datasets