Using the FeatureExtraction package in HADES, create a CovariateData data object for the study cohort

[jaygee-on-github]

• Utilizes the OHDSI FeatureExtraction package to pull candidate covariates from the OMOP CDM for downstream machine learning applications in No Code/Low Code environments

• Enhances the feature extraction process to remove infrequent covariates, normalize, and reduce redundancy for machine learning experiments

• Depends on Grow the Ethiopia Study #2 in the AWS Data Enclave to include BMI, WHOQoL, WHODAS and other available determinants of health from the source data #2 and Using ATLAS create the Ethiopia Study #2 study cohort #3.

• Think of this data object as a table of covariate/predictor candidates we will filter in a subsequent task which performs FeatureSelection

• Assigned to @lnajjemba