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# Retrieve Antimicrobial Drug Names and Doses from Clinical Text

Source: `R/ab_from_text.R`
`ab_from_text.Rd`

Use this function on e.g. clinical texts from health care records. It returns a list with all antimicrobial drugs, doses and forms of administration found in the texts.

## Usage

    ab_from_text(
      text,
      type = c("drug", "dose", "administration"),
      collapse = NULL,
      translate_ab = FALSE,
      thorough_search = NULL,
      info = interactive(),
      ...
    )

## Arguments

text

  Text to analyse.

type

  Type of property to search for, either `"drug"`, `"dose"` or `"administration"`, see Examples.

collapse

  A character to pass on to `paste(, collapse = ...)` to only return one character per element of `text`, see Examples.

translate_ab

  If `type = "drug"`: a column name of the antimicrobials data set to translate the antibiotic abbreviations to, using `ab_property()`. The default is `FALSE`. Using `TRUE` is equal to using "name".

thorough_search

  A logical to indicate whether the input must be extensively searched for misspelling and other faulty input values. Setting this to `TRUE` will take considerably more time than when using `FALSE`. At default, it will turn `TRUE` when all input elements contain a maximum of three words.

info

  A logical to indicate whether a progress bar should be printed - the default is `TRUE` only in interactive mode.

...

  Arguments passed on to `as.ab()`.

## Value

A list, or a character if `collapse` is not `NULL`

## Details

This function is also internally used by `as.ab()`, although it then only searches for the first drug name and will throw a note if more drug names could have been returned. Note: the `as.ab()` function may use very long regular expression to match brand names of antimicrobial drugs. This may fail on some systems.

### Argument `type`

At default, the function will search for antimicrobial drug names. All text elements will be searched for official names, ATC codes and brand names. As it uses `as.ab()` internally, it will correct for misspelling.

With `type = "dose"` (or similar, like "dosing", "doses"), all text elements will be searched for numeric values that are higher than 100 and do not resemble years. The output will be numeric. It supports any unit (g, mg, IE, etc.) and multiple values in one clinical text, see Examples.

With `type = "administration"` (or abbreviations, like "admin", "adm"), all text elements will be searched for a form of drug administration. It supports the following forms (including common abbreviations): buccal, implant, inhalation, instillation, intravenous, nasal, oral, parenteral, rectal, sublingual, transdermal and vaginal. Abbreviations for oral (such as 'po', 'per os') will become "oral", all values for intravenous (such as 'iv', 'intraven') will become "iv". It supports multiple values in one clinical text, see Examples.

### Argument `collapse`

Without using `collapse`, this function will return a list. This can be convenient to use e.g. inside a `mutate()`):
`df %>% mutate(abx = ab_from_text(clinical_text))`

The returned AB codes can be transformed to official names, groups, etc. with all `ab_*` functions such as `ab_name()` and `ab_group()`, or by using the `translate_ab` argument.

With using `collapse`, this function will return a character:
`df %>% mutate(abx = ab_from_text(clinical_text, collapse = "|"))`

## Examples

    # mind the bad spelling of amoxicillin in this line,
    # straight from a true health care record:
    ab_from_text("28/03/2020 regular amoxicilliin 500mg po tid")
    #> [[1]]
    #> Class <ab>
    #> [1] AMX
    #>

    ab_from_text("500 mg amoxi po and 400mg cipro iv")
    #> [[1]]
    #> Class <ab>
    #> [1] AMX CIP
    #>
    ab_from_text("500 mg amoxi po and 400mg cipro iv", type = "dose")
    #> [[1]]
    #> [1] 500 400
    #>
    ab_from_text("500 mg amoxi po and 400mg cipro iv", type = "admin")
    #> [[1]]
    #> [1] "oral" "iv"
    #>

    ab_from_text("500 mg amoxi po and 400mg cipro iv", collapse = ", ")
    #> [1] "AMX, CIP"
    # \donttest{
    # if you want to know which antibiotic groups were administered, do e.g.:
    abx <- ab_from_text("500 mg amoxi po and 400mg cipro iv")
    ab_group(abx[[1]])
    #> [1] "Aminopenicillins" "Fluoroquinolones"

    if (require("dplyr")) {
      tibble(clinical_text = c(
        "given 400mg cipro and 500 mg amox",
        "started on doxy iv today"
      )) %>%
        mutate(
          abx_codes = ab_from_text(clinical_text),
          abx_doses = ab_from_text(clinical_text, type = "doses"),
          abx_admin = ab_from_text(clinical_text, type = "admin"),
          abx_coll = ab_from_text(clinical_text, collapse = "|"),
          abx_coll_names = ab_from_text(clinical_text,
            collapse = "|",
            translate_ab = "name"
          ),
          abx_coll_doses = ab_from_text(clinical_text,
            type = "doses",
            collapse = "|"
          ),
          abx_coll_admin = ab_from_text(clinical_text,
            type = "admin",
            collapse = "|"
          )
        )
    }
    #> Loading required package: dplyr
    #>
    #> Attaching package: ‘dplyr’
    #> The following objects are masked from ‘package:stats’:
    #>
    #>     filter, lag
    #> The following objects are masked from ‘package:base’:
    #>
    #>     intersect, setdiff, setequal, union
    #> # A tibble: 2 × 8
    #>   clinical_text            abx_codes abx_doses abx_admin abx_coll abx_coll_names
    #>   <chr>                    <list>    <list>    <list>    <chr>    <chr>
    #> 1 given 400mg cipro and 5… <ab [2]>  <dbl [2]> <chr [1]> CIP|AMX  Ciprofloxacin…
    #> 2 started on doxy iv today <ab [1]>  <dbl [1]> <chr [1]> DOX      Doxycycline
    #> # ℹ 2 more variables: abx_coll_doses <chr>, abx_coll_admin <chr>
    # }