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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%",
message=FALSE,
warning=FALSE
)
```
# CodelistGenerator <img src="man/figures/logo.png" align="right" height="200"/>
<!-- badges: start -->
[](https://CRAN.R-project.org/package=CodelistGenerator)
[](https://app.codecov.io/github/darwin-eu/CodelistGenerator?branch=main)
[](https://github.com/darwin-eu/CodelistGenerator/actions)
[](https://lifecycle.r-lib.org/articles/stages.html)
[](https://github.com/darwin-eu/CodelistGenerator/actions/workflows/R-CMD-check.yaml)
<!-- badges: end -->
## Installation
You can install CodelistGenerator from CRAN
``` r
install.packages("CodelistGenerator")
```
Or you can also install the development version of CodelistGenerator
``` r
install.packages("remotes")
remotes::install_github("darwin-eu/CodelistGenerator")
```
## Example usage
```{r}
library(dplyr)
library(CDMConnector)
library(CodelistGenerator)
```
For this example we'll use the Eunomia dataset (which only contains a subset of the OMOP CDM vocabularies)
```{r}
db <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomia_dir())
cdm <- cdm_from_con(db, cdm_schema = "main", write_schema = c(prefix = "cg_", schema = "main"))
```
## Exploring the OMOP CDM Vocabulary tables
OMOP CDM vocabularies are frequently updated, and we can identify the version of the vocabulary of our Eunomia data
```{r}
getVocabVersion(cdm = cdm)
```
CodelistGenerator provides various other functions to explore the vocabulary tables. For example, we can see the the different concept classes of standard concepts used for drugs
```{r}
getConceptClassId(cdm,
standardConcept = "Standard",
domain = "Drug")
```
## Vocabulary based codelists using CodelistGenerator
CodelistGenerator provides functions to extract code lists based on vocabulary hierarchies. One example is `getDrugIngredientCodes, which we can use, for example, to get all the concept IDs used to represent aspirin.
```{r}
getDrugIngredientCodes(cdm = cdm, name = "aspirin")
```
If we also want the details of these concept IDs we can get these like so.
```{r}
getDrugIngredientCodes(cdm = cdm, name = "aspirin", withConceptDetails = TRUE)
```
And if we want codelists for all drug ingredients we can simply omit the name argument and all ingredients will be returned.
```{r}
ing <- getDrugIngredientCodes(cdm = cdm)
ing$aspirin
ing$diclofenac
ing$celecoxib
```
## Systematic search using CodelistGenerator
CodelistGenerator can also support systematic searches of the vocabulary tables to support codelist development. A little like the process for a systematic review, the idea is that for a specified search strategy, CodelistGenerator will identify a set of concepts that may be relevant, with these then being screened to remove any irrelevant codes by clinical experts.
We can do a simple search for asthma
```{r}
asthma_codes1 <- getCandidateCodes(
cdm = cdm,
keywords = "asthma",
domains = "Condition"
)
asthma_codes1 %>%
glimpse()
```
But perhaps we want to exclude certain concepts as part of the search strategy, in this case we can add these like so
```{r}
asthma_codes2 <- getCandidateCodes(
cdm = cdm,
keywords = "asthma",
exclude = "childhood",
domains = "Condition"
)
asthma_codes2 %>%
glimpse()
```
We can compare these two code lists like so
```{r}
compareCodelists(asthma_codes1, asthma_codes2)
```
We can then also see non-standard codes these are mapped from, for example here we can see the non-standard ICD10 code that maps to a standard snowmed code for gastrointestinal hemorrhage returned by our search
```{r}
Gastrointestinal_hemorrhage <- getCandidateCodes(
cdm = cdm,
keywords = "Gastrointestinal hemorrhage",
domains = "Condition"
)
Gastrointestinal_hemorrhage %>%
glimpse()
```
## Summarising code use
```{r}
summariseCodeUse(list("asthma" = asthma_codes1$concept_id),
cdm = cdm) %>%
glimpse()
```
```{r, echo=FALSE}
DBI::dbDisconnect(db)
```