Retrieve Antimicrobial Drug Names and Doses from Clinical Text

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 antibiotics data set to translate the antibiotic abbreviations to, using ab_property(). Defaults to 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, defaults to 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 agents. 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] "Beta-lactams/penicillins" "Quinolones"              

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_c…¹ abx_d…² abx_a…³ abx_c…⁴ abx_c…⁵ abx_c…⁶ abx_c…⁷
#>   <chr>                  <list>  <list>  <list>  <chr>   <chr>   <chr>   <chr>  
#> 1 given 400mg cipro and… <ab>    <dbl>   <chr>   CIP|AMX Ciprof… 400|500 NA     
#> 2 started on doxy iv to… <ab>    <dbl>   <chr>   DOX     Doxycy… NA      iv     
#> # … with abbreviated variable names ¹abx_codes, ²abx_doses, ³abx_admin,
#> #   ⁴abx_coll, ⁵abx_coll_names, ⁶abx_coll_doses, ⁷abx_coll_admin
# }