The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
In this example we’re going to again create cohorts of individuals with an ankle sprain, ankle fracture, forearm fracture, or a hip fracture using the Eunomia synthetic data.
library(CDMConnector)
library(CohortConstructor)
library(CodelistGenerator)
library(PatientProfiles)
library(CohortCharacteristics)
library(PhenotypeR)
library(dplyr)
library(ggplot2)
con <- DBI::dbConnect(duckdb::duckdb(),
CDMConnector::eunomiaDir("synpuf-1k", "5.3"))
cdm <- CDMConnector::cdmFromCon(con = con,
cdmName = "Eunomia Synpuf",
cdmSchema = "main",
writeSchema = "main",
achillesSchema = "main")
cdm$injuries <- conceptCohort(cdm = cdm,
conceptSet = list(
"ankle_sprain" = 81151,
"ankle_fracture" = 4059173,
"forearm_fracture" = 4278672,
"hip_fracture" = 4230399
),
name = "injuries")
Running the matchedDiagnostics()
will compare the
individuals in our cohorts with age and sex matched controls from the
data source. This helps us to find features of our cohort that are
particularly distinctive.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.