# Generate a candidate codelist

In this example we will create a candidate codelist for osteoarthritis, exploring how different search strategies may impact our final codelist. First, let’s load the necessary packages and create a cdm reference using mock data.

    library(dplyr)
    library(CodelistGenerator)

    cdm <- mockVocabRef()

The mock data has the following hypothetical concepts and relationships:

## Search for keyword match

We will start by creating a codelist with keywords match. Let’s say that we want to find those codes that contain “Musculoskeletal disorder” in their concept_name:

    getCandidateCodes(
      cdm = cdm,
      keywords = "Musculoskeletal disorder",
      domains = "Condition",
      standardConcept = "Standard",
      includeDescendants = FALSE,
      searchInSynonyms = FALSE,
      searchNonStandard = FALSE,
      includeAncestor = FALSE
    )
    #> # A tibble: 1 × 6
    #>   concept_id found_from    concept_name domain_id vocabulary_id standard_concept
    #>        <int> <chr>         <chr>        <chr>     <chr>         <chr>
    #> 1          1 From initial… Musculoskel… Condition SNOMED        S

Note that we could also identify it based on a partial match or based on all combinations match.

    getCandidateCodes(
      cdm = cdm,
      keywords = "Musculoskeletal",
      domains = "Condition",
      standardConcept = "Standard",
      searchInSynonyms = FALSE,
      searchNonStandard = FALSE,
      includeDescendants = FALSE,
      includeAncestor = FALSE
    )
    #> # A tibble: 1 × 6
    #>   concept_id found_from    concept_name domain_id vocabulary_id standard_concept
    #>        <int> <chr>         <chr>        <chr>     <chr>         <chr>
    #> 1          1 From initial… Musculoskel… Condition SNOMED        S

    getCandidateCodes(
      cdm = cdm,
      keywords = "Disorder musculoskeletal",
      domains = "Condition",
      standardConcept = "Standard",
      searchInSynonyms = FALSE,
      searchNonStandard = FALSE,
      includeDescendants = FALSE,
      includeAncestor = FALSE
    )
    #> # A tibble: 1 × 6
    #>   concept_id found_from    concept_name domain_id vocabulary_id standard_concept
    #>        <int> <chr>         <chr>        <chr>     <chr>         <chr>
    #> 1          1 From initial… Musculoskel… Condition SNOMED        S

Notice that currently we are only looking for concepts with `domain = "Condition"`. However, we can expand the search to all domains using `domain = NULL`.

`getCandidateCodes()` function will generate a table with class “candidate_codes”, which contains an atribute with the details of the search strategy:

    candidate_codes <- getCandidateCodes(
      cdm = cdm,
      keywords = "Musculoskeletal",
      domains = "Condition",
      standardConcept = "Standard",
      searchInSynonyms = FALSE,
      searchNonStandard = FALSE,
      includeDescendants = FALSE,
      includeAncestor = FALSE
    )

    searchStrategy(candidate_codes)
    #> # A tibble: 1 × 10
    #>   cdm_name vocabulary_version keywords          exclude domains standard_concept
    #>   <chr>    <chr>              <chr>             <chr>   <chr>   <chr>
    #> 1 mock     v5.0 22-JUN-22     "\"Musculoskelet… "\"\""  "\"con… "\"Standard\""
    #> # ℹ 4 more variables: search_in_synonyms <lgl>, search_non_standard <lgl>,
    #> #   include_descendants <lgl>, include_ancestor <lgl>

## Include non-standard concepts

Now we will include standard and non-standard concepts in our initial search. By setting `standardConcept = c("Non-standard", "Standard")`, we allow the function to return, in the final candidate codelist, both the non-standard and standard codes that have been found.

    getCandidateCodes(
      cdm = cdm,
      keywords = "Musculoskeletal disorder",
      domains = "Condition",
      standardConcept = c("Non-standard", "Standard"),
      searchInSynonyms = FALSE,
      searchNonStandard = FALSE,
      includeDescendants = FALSE,
      includeAncestor = FALSE
    )
    #> # A tibble: 2 × 6
    #>   concept_id found_from    concept_name domain_id vocabulary_id standard_concept
    #>        <int> <chr>         <chr>        <chr>     <chr>         <chr>
    #> 1          1 From initial… Musculoskel… Condition SNOMED        S
    #> 2         24 From initial… Other muscu… Condition SNOMED        <NA>

## Multiple search terms

We can also search for multiple keywords simultaneously, capturing all of them with the following search:

    getCandidateCodes(
      cdm = cdm,
      keywords = c(
        "Musculoskeletal disorder",
        "arthritis"
      ),
      domains = "Condition",
      standardConcept = c("Standard"),
      includeDescendants = FALSE,
      searchInSynonyms = FALSE,
      searchNonStandard = FALSE,
      includeAncestor = FALSE
    )
    #> # A tibble: 4 × 6
    #>   concept_id found_from    concept_name domain_id vocabulary_id standard_concept
    #>        <int> <chr>         <chr>        <chr>     <chr>         <chr>
    #> 1          1 From initial… Musculoskel… Condition SNOMED        S
    #> 2          3 From initial… Arthritis    Condition SNOMED        S
    #> 3          4 From initial… Osteoarthri… Condition SNOMED        S
    #> 4          5 From initial… Osteoarthri… Condition SNOMED        S

## Add descendants

Now we will include the descendants of an identified code using `includeDescendants` argument

    getCandidateCodes(
      cdm = cdm,
      keywords = "Musculoskeletal disorder",
      domains = "Condition",
      standardConcept = "Standard",
      includeDescendants = TRUE,
      searchInSynonyms = FALSE,
      searchNonStandard = FALSE,
      includeAncestor = FALSE
    )
    #> # A tibble: 5 × 6
    #>   concept_id found_from    concept_name domain_id vocabulary_id standard_concept
    #>        <int> <chr>         <chr>        <chr>     <chr>         <chr>
    #> 1          1 From initial… Musculoskel… Condition SNOMED        S
    #> 2          2 From descend… Osteoarthro… Condition SNOMED        S
    #> 3          3 From descend… Arthritis    Condition SNOMED        S
    #> 4          4 From descend… Osteoarthri… Condition SNOMED        S
    #> 5          5 From descend… Osteoarthri… Condition SNOMED        S

Notice that now, in the column `found_from`, we can see that we have obtain `concept_id=1` from an initial search, and `concept_id_=c(2,3,4,5)` when searching for descendants of concept_id 1.

## With exclusions

We can also exclude specific keywords using the argument `exclude`

    getCandidateCodes(
      cdm = cdm,
      keywords = "Musculoskeletal disorder",
      domains = "Condition",
      exclude = c("Osteoarthrosis", "knee"),
      standardConcept = "Standard",
      includeDescendants = TRUE,
      searchInSynonyms = FALSE,
      searchNonStandard = FALSE,
      includeAncestor = FALSE
    )
    #> # A tibble: 3 × 6
    #>   concept_id found_from    concept_name domain_id vocabulary_id standard_concept
    #>        <int> <chr>         <chr>        <chr>     <chr>         <chr>
    #> 1          1 From initial… Musculoskel… Condition SNOMED        S
    #> 2          3 From descend… Arthritis    Condition SNOMED        S
    #> 3          5 From descend… Osteoarthri… Condition SNOMED        S

When multiple words are added within a term (e.g., “knee osteoarthritis”), each word will be searched independently, so that for example, “osteoarthritis of knee” is excluded:

    getCandidateCodes(
      cdm = cdm,
      keywords = "Musculoskeletal disorder",
      domains = "Condition",
      exclude = c("knee osteoarthritis"),
      standardConcept = "Standard",
      includeDescendants = TRUE,
      searchInSynonyms = FALSE,
      searchNonStandard = FALSE,
      includeAncestor = FALSE
    )
    #> # A tibble: 4 × 6
    #>   concept_id found_from    concept_name domain_id vocabulary_id standard_concept
    #>        <int> <chr>         <chr>        <chr>     <chr>         <chr>
    #> 1          1 From initial… Musculoskel… Condition SNOMED        S
    #> 2          2 From descend… Osteoarthro… Condition SNOMED        S
    #> 3          3 From descend… Arthritis    Condition SNOMED        S
    #> 4          5 From descend… Osteoarthri… Condition SNOMED        S

If we only want to exclude exact matching terms (without accounting for words boundaries) we need to add “/” at the beginning and at the end of the term. Hence, using “knee osteoarthritis”, “osteoarthritis of knee” won’t be excluded. However, if we had “rightknee osteoarthritis”, it would be excluded.

    # No exclusion:
    getCandidateCodes(
        cdm = cdm,
        keywords = "Knee",
        domains = "Condition",
        exclude = NULL,
        standardConcept = c("Standard", "Non-standard"),
        includeDescendants = TRUE,
        searchInSynonyms = FALSE,
        searchNonStandard = FALSE,
        includeAncestor = FALSE
    )
    #> # A tibble: 2 × 6
    #>   concept_id found_from    concept_name domain_id vocabulary_id standard_concept
    #>        <int> <chr>         <chr>        <chr>     <chr>         <chr>
    #> 1          4 From initial… Osteoarthri… Condition SNOMED        S
    #> 2          8 From initial… Knee osteoa… Condition Read          <NA>

    # Exclusion looking for terms:
    getCandidateCodes(
      cdm = cdm,
      keywords = "Knee",
      domains = "Condition",
      exclude = c("knee osteoarthritis"),
      standardConcept = c("Standard", "Non-standard"),
      includeDescendants = TRUE,
      searchInSynonyms = FALSE,
      searchNonStandard = FALSE,
      includeAncestor = FALSE
    )
    #> # A tibble: 0 × 7
    #> # ℹ 7 variables: concept_id <int>, found_from <chr>, found_id <int>,
    #> #   concept_name <chr>, domain_id <chr>, vocabulary_id <chr>,
    #> #   standard_concept <chr>

    # Exclusion looking for partial matching terms (without word boundaries)
    getCandidateCodes(
      cdm = cdm,
      keywords = "Knee",
      domains = "Condition",
      exclude = c("/knee osteoarthritis/"),
      standardConcept = c("Standard", "Non-standard"),
      includeDescendants = TRUE,
      searchInSynonyms = FALSE,
      searchNonStandard = FALSE,
      includeAncestor = FALSE
    )
    #> # A tibble: 1 × 6
    #>   concept_id found_from    concept_name domain_id vocabulary_id standard_concept
    #>        <int> <chr>         <chr>        <chr>     <chr>         <chr>
    #> 1          4 From initial… Osteoarthri… Condition SNOMED        S

    # Exclusion looking for partial matching terms (without word boundaries)
    getCandidateCodes(
      cdm = cdm,
      keywords = "Knee",
      domains = "Condition",
      exclude = c("/e osteoarthritis/"),
      standardConcept = c("Standard", "Non-standard"),
      includeDescendants = TRUE,
      searchInSynonyms = FALSE,
      searchNonStandard = FALSE,
      includeAncestor = FALSE
    )
    #> # A tibble: 1 × 6
    #>   concept_id found_from    concept_name domain_id vocabulary_id standard_concept
    #>        <int> <chr>         <chr>        <chr>     <chr>         <chr>
    #> 1          4 From initial… Osteoarthri… Condition SNOMED        S

If we want to do exact matching (that means, to find the exact two words “knee osteoarthritis” in the concept name) we need to use “/ at the beginning and at the end of the expression.

    getCandidateCodes(
      cdm = cdm,
      keywords = "Knee",
      domains = "Condition",
      exclude = c("/\bKnee osteoarthritis/\b"),
      standardConcept = c("Standard", "Non-standard"),
      includeDescendants = TRUE,
      searchInSynonyms = FALSE,
      searchNonStandard = FALSE,
      includeAncestor = FALSE
    )
    #> # A tibble: 1 × 6
    #>   concept_id found_from    concept_name domain_id vocabulary_id standard_concept
    #>        <int> <chr>         <chr>        <chr>     <chr>         <chr>
    #> 1          4 From initial… Osteoarthri… Condition SNOMED        S

    # We will now only search for "ee osteoarthritis" to show that
    # "knee osteoarthritis" won't be excluded:
    getCandidateCodes(
      cdm = cdm,
      keywords = "Knee",
      domains = "Condition",
      exclude = c("/\bee osteoarthritis/\b"),
      standardConcept = c("Standard", "Non-standard"),
      includeDescendants = TRUE,
      searchInSynonyms = FALSE,
      searchNonStandard = FALSE,
      includeAncestor = FALSE
    )
    #> # A tibble: 2 × 6
    #>   concept_id found_from    concept_name domain_id vocabulary_id standard_concept
    #>        <int> <chr>         <chr>        <chr>     <chr>         <chr>
    #> 1          4 From initial… Osteoarthri… Condition SNOMED        S
    #> 2          8 From initial… Knee osteoa… Condition Read          <NA>

    For example, if we look for

    Notice that, for example, if we wanted `keywords = "depression"` and `exclude = "ST depression"`, concepts like "poSTpartum depression" would be excluded. To avoid this,
    we could use `exclude = "/ST depression/"`. Notice that, "poST depression" would also be excluded with this option.

    Hence, there is another option to exclude exact matching terms accounting for words boundaries: adding "/\b" at the beginning and at the end of the term. For example, if we look for "/\bp osteoarthritis/\b", concepts like "hip osteoarthritis **won't** be excluded.

    ## Add ancestor
    To include the ancestors one level above the identified concepts, we can use the argument `includeAncestor`
    <img src="Figures/7.png" width="100%" />

    ``` r
    codes <- getCandidateCodes(
      cdm = cdm,
      keywords = "Osteoarthritis of knee",
      includeAncestor = TRUE,
      domains = "Condition",
      standardConcept = "Standard",
      includeDescendants = TRUE,
      searchInSynonyms = FALSE,
      searchNonStandard = FALSE,
    )

    codes
    #> # A tibble: 2 × 6
    #>   concept_id found_from    concept_name domain_id vocabulary_id standard_concept
    #>        <int> <chr>         <chr>        <chr>     <chr>         <chr>
    #> 1          3 From ancestor Arthritis    Condition SNOMED        S
    #> 2          4 From initial… Osteoarthri… Condition SNOMED        S

## Search using synonyms

We can also pick up codes based on their synonyms. For example, Osteoarthrosis has a synonym of Arthritis.

    getCandidateCodes(
      cdm = cdm,
      keywords = "osteoarthrosis",
      domains = "Condition",
      searchInSynonyms = TRUE,
      standardConcept = "Standard",
      includeDescendants = FALSE,
      searchNonStandard = FALSE,
      includeAncestor = FALSE
    )
    #> # A tibble: 2 × 6
    #>   concept_id found_from    concept_name domain_id vocabulary_id standard_concept
    #>        <int> <chr>         <chr>        <chr>     <chr>         <chr>
    #> 1          2 From initial… Osteoarthro… Condition SNOMED        S
    #> 2          3 In synonyms   Arthritis    Condition SNOMED        S

Notice that if `includeDescendants = TRUE`, Arthritis descendants will also be included:

    getCandidateCodes(
      cdm = cdm,
      keywords = "osteoarthrosis",
      domains = "Condition",
      searchInSynonyms = TRUE,
      standardConcept = "Standard",
      includeDescendants = TRUE,
      searchNonStandard = FALSE,
      includeAncestor = FALSE
    )
    #> # A tibble: 4 × 6
    #>   concept_id found_from    concept_name domain_id vocabulary_id standard_concept
    #>        <int> <chr>         <chr>        <chr>     <chr>         <chr>
    #> 1          2 From initial… Osteoarthro… Condition SNOMED        S
    #> 2          3 In synonyms   Arthritis    Condition SNOMED        S
    #> 3          4 From descend… Osteoarthri… Condition SNOMED        S
    #> 4          5 From descend… Osteoarthri… Condition SNOMED        S

## Search via non-standard

We can also pick up concepts associated with our keyword via non-standard search.

    codes1 <- getCandidateCodes(
      cdm = cdm,
      keywords = "Degenerative",
      domains = "Condition",
      standardConcept = "Standard",
      searchNonStandard = TRUE,
      includeDescendants = FALSE,
      searchInSynonyms = FALSE,
      includeAncestor = FALSE
    )
    codes1
    #> # A tibble: 1 × 6
    #>   concept_id found_from    concept_name domain_id vocabulary_id standard_concept
    #>        <int> <chr>         <chr>        <chr>     <chr>         <chr>
    #> 1          2 From non-sta… Osteoarthro… Condition SNOMED        S

Let’s take a moment to focus on the `standardConcept` and `searchNonStandard` arguments to clarify the difference between them. `standardConcept` specifies whether we want only standard concepts or also include non-standard concepts in the final candidate codelist. `searchNonStandard` determines whether we want to search for keywords among non-standard concepts.

In the previous example, since we set `standardConcept = "Standard"`, we retrieved the code for Osteoarthrosis from the non-standard search. However, we did not obtain the non-standard code degenerative arthropathy from the initial search. If we allow non-standard concepts in the final candidate codelist, we would retireve both codes:

    codes2 <- getCandidateCodes(
      cdm = cdm,
      keywords = "Degenerative",
      domains = "Condition",
      standardConcept = c("Non-standard", "Standard"),
      searchNonStandard = FALSE,
      includeDescendants = FALSE,
      searchInSynonyms = FALSE,
      includeAncestor = FALSE
    )
    codes2
    #> # A tibble: 1 × 6
    #>   concept_id found_from    concept_name domain_id vocabulary_id standard_concept
    #>        <int> <chr>         <chr>        <chr>     <chr>         <chr>
    #> 1          7 From initial… Degenerativ… Condition Read          <NA>