Systems and methods for converting an electronic data capture dataset to a standard data tabulation model (SDTM) dataset

A system for converting an Electronic Data Capture (EDC) dataset to a Standard Data Tabulation Model (SDTM) dataset, comprising:

• a computer having one or more processors in communication with a memory, the memory storing instructions executable by said one or more processors to perform:
• processing metadata for an EDC dataset, the metadata comprising EDC field names, to produce vectors of dimensionality n1, where n1 is an integer;
• processing the vectors of dimensionality n1, in an embedding model, to produce embedded vectors of dimensionality n2, where n2 is an integer and is less than n1;
• training a classification model using a subset of the metadata that is pre-mapped to a subset of the SDTM field names, wherein the subset of the metadata is processed by the embedding model before being input to the classification model;
• processing the embedded vectors, in the trained classification model, to produce class predictions for the EDC field names, the classes corresponding to the SDTM field names;
• mapping the EDC field names, respectively, with the SDTM field names based at least in part on the class predictions for the EDC field names;
• and
• converting the EDC dataset to an SDTM dataset based at least in part on said mapping,
• wherein the embedding model is obtained from a trained Siamese neural network comprising a first embedding subnetwork and a second embedding subnetwork.

A non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a computer, cause said one or more processors to perform a method of converting an Electronic Data Capture (EDC) dataset to a Standard Data Tabulation Model (SDTM) dataset, the method comprising:

• processing metadata for an EDC dataset, the metadata comprising EDC field names, to produce vectors of dimensionality n1, where n1 is an integer;
• processing the vectors of dimensionality n1, in an embedding model, to produce embedded vectors of dimensionality n2, where n2 is an integer and is less than n1;
• training a classification model using a subset of the metadata that is pre-mapped to a subset of the SDTM field names, wherein the subset of the metadata is processed by the embedding model before being input to the classification model;
• processing the embedded vectors, in the trained classification model, to produce class predictions for the EDC field names, the classes corresponding to the SDTM field names;
• mapping the EDC field names, respectively, with the SDTM field names based at least in part on the class predictions for the EDC field names;
• and
• converting the EDC dataset to an SDTM dataset based at least in part on said mapping,
• wherein the embedding model is obtained from a trained Siamese neural network comprising a first embedding subnetwork and a second embedding subnetwork.