You could look at the output of the createDefaultCovariateSettings()
function:
createDefaultCovariateSettings()
$temporal
[1] FALSE
$DemographicsGender
[1] TRUE
$DemographicsAgeGroup
[1] TRUE
$DemographicsRace
[1] TRUE
$DemographicsEthnicity
[1] TRUE
$DemographicsIndexYear
[1] TRUE
$DemographicsIndexMonth
[1] TRUE
$ConditionGroupEraLongTerm
[1] TRUE
$ConditionGroupEraShortTerm
[1] TRUE
$DrugGroupEraLongTerm
[1] TRUE
$DrugGroupEraShortTerm
[1] TRUE
$DrugGroupEraOverlapping
[1] TRUE
$ProcedureOccurrenceLongTerm
[1] TRUE
$ProcedureOccurrenceShortTerm
[1] TRUE
$DeviceExposureLongTerm
[1] TRUE
$DeviceExposureShortTerm
[1] TRUE
$MeasurementLongTerm
[1] TRUE
$MeasurementShortTerm
[1] TRUE
$MeasurementRangeGroupLongTerm
[1] TRUE
$ObservationLongTerm
[1] TRUE
$ObservationShortTerm
[1] TRUE
$CharlsonIndex
[1] TRUE
$Dcsi
[1] TRUE
$Chads2
[1] TRUE
$Chads2Vasc
[1] TRUE
$includedCovariateConceptIds
logical(0)
$includedCovariateIds
logical(0)
$addDescendantsToInclude
[1] FALSE
$excludedCovariateConceptIds
logical(0)
$addDescendantsToExclude
[1] FALSE
$shortTermStartDays
[1] -30
$mediumTermStartDays
[1] -180
$endDays
[1] 0
$longTermStartDays
[1] -365
attr(,"fun")
[1] "getDbDefaultCovariateData"
attr(,"class")
[1] "covariateSettings"
There’s a short description of each analysis (e.g. ‘ObservationLongTerm’) in the documentation of the createCovariateSettings()
function.
If you really want to dive all the way into the bowels of FeatureExtraction, you can look at the CSV file specifying the analyses, and look at those rows with TRUE
in the ‘isDefault’ column. This CSV file further references the template SQL files in the sql folder. But be warned, because the SQL is very hard to read because it serves many use cases (aggregated or not, temporal or not).