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# ==================================================================== #
# TITLE #
# Antimicrobial Resistance (AMR) Analysis #
# #
# SOURCE #
# https://github.com/msberends/AMR #
# #
# LICENCE #
# (c) 2018-2020 Berends MS, Luz CF et al. #
# #
# This R package is free software; you can freely use and distribute #
# it for both personal and commercial purposes under the terms of the #
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
# the Free Software Foundation. #
# #
# We created this package for both routine data analysis and academic #
# research and it was publicly released in the hope that it will be #
# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
# Visit our website for more info: https://msberends.github.io/AMR. #
# ==================================================================== #
#' Transform to antibiotic ID
#'
#' Use this function to determine the antibiotic code of one or more antibiotics. The data set [antibiotics] will be searched for abbreviations, official names and synonyms (brand names).
#' @inheritSection lifecycle Maturing lifecycle
#' @param x character vector to determine to antibiotic ID
#' @param flag_multiple_results logical to indicate whether a note should be printed to the console that probably more than one antibiotic code or name can be retrieved from a single input value.
#' @param ... arguments passed on to internal functions
#' @rdname as.ab
#' @inheritSection WHOCC WHOCC
#' @details All entries in the [antibiotics] data set have three different identifiers: a human readable EARS-Net code (column `ab`, used by ECDC and WHONET), an ATC code (column `atc`, used by WHO), and a CID code (column `cid`, Compound ID, used by PubChem). The data set contains more than 5,000 official brand names from many different countries, as found in PubChem.
#'
#' All these properties will be searched for the user input. The [as.ab()] can correct for different forms of misspelling:
#'
#' * Wrong spelling of drug names (like "tobramicin" or "gentamycin"), which corrects for most audible similarities such as f/ph, x/ks, c/z/s, t/th, etc.
#' * Too few or too many vowels or consonants
#' * Switching two characters (like "mreopenem", often the case in clinical data, when doctors typed too fast)
#' * Digitalised paper records, leaving artefacts like 0/o/O (zero and O's), B/8, n/r, etc.
#'
#' Use the [ab_property()] functions to get properties based on the returned antibiotic ID, see Examples.
#'
#' @section Source:
#' World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology: \url{https://www.whocc.no/atc_ddd_index/}
#'
#' WHONET 2019 software: \url{http://www.whonet.org/software.html}
#'
#' European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: \url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}
#' @aliases ab
#' @return Character (vector) with class [`ab`]. Unknown values will return `NA`.
#' @seealso
#' * [antibiotics] for the dataframe that is being used to determine ATCs
#' * [ab_from_text()] for a function to retrieve antimicrobial drugs from clinical text (from health care records)
#' @inheritSection AMR Read more on our website!
#' @export
#' @examples
#' # these examples all return "ERY", the ID of erythromycin:
#' as.ab("J01FA01")
#' as.ab("J 01 FA 01")
#' as.ab("Erythromycin")
#' as.ab("eryt")
#' as.ab(" eryt 123")
#' as.ab("ERYT")
#' as.ab("ERY")
#' as.ab("eritromicine") # spelled wrong, yet works
#' as.ab("Erythrocin") # trade name
#' as.ab("Romycin") # trade name
#'
#' # spelling from different languages and dyslexia are no problem
#' ab_atc("ceftriaxon")
#' ab_atc("cephtriaxone") # small spelling error
#' ab_atc("cephthriaxone") # or a bit more severe
#' ab_atc("seephthriaaksone") # and even this works
#'
#' # use ab_* functions to get a specific properties (see ?ab_property);
#' # they use as.ab() internally:
#' ab_name("J01FA01") # "Erythromycin"
#' ab_name("eryt") # "Erythromycin"
as.ab <- function(x, flag_multiple_results = TRUE, ...) {
check_dataset_integrity()
if (is.ab(x)) {
return(x)
}
initial_search <- is.null(list(...)$initial_search)
already_regex <- isTRUE(list(...)$already_regex)
if (all(toupper(x) %in% antibiotics$ab)) {
# valid AB code, but not yet right class
return(structure(.Data = toupper(x),
class = c("ab", "character")))
}
x_bak <- x
x <- toupper(x)
# remove diacritics
x <- iconv(x, from = "UTF-8", to = "ASCII//TRANSLIT")
x <- gsub('"', "", x, fixed = TRUE)
x_bak_clean <- x
if (already_regex == FALSE) {
# remove suffices
x_bak_clean <- gsub("_(MIC|RSI|DIS[CK])$", "", x_bak_clean)
# remove disk concentrations, like LVX_NM -> LVX
x_bak_clean <- gsub("_[A-Z]{2}[0-9_.]{0,3}$", "", x_bak_clean)
# remove part between brackets if that's followed by another string
x_bak_clean <- gsub("(.*)+ [(].*[)]", "\\1", x_bak_clean)
# keep only max 1 space
x_bak_clean <- trimws(gsub(" +", " ", x_bak_clean))
# non-character, space or number should be a slash
x_bak_clean <- gsub("[^A-Z0-9 -]", "/", x_bak_clean)
# spaces around non-characters must be removed: amox + clav -> amox/clav
x_bak_clean <- gsub("(.*[A-Z0-9]) ([^A-Z0-9].*)", "\\1\\2", x_bak_clean)
x_bak_clean <- gsub("(.*[^A-Z0-9]) ([A-Z0-9].*)", "\\1\\2", x_bak_clean)
# remove hyphen after a starting "co"
x_bak_clean <- gsub("^CO-", "CO", x_bak_clean)
# replace text 'and' with a slash
x_bak_clean <- gsub(" AND ", "/", x_bak_clean)
}
x <- unique(x_bak_clean)
x_new <- rep(NA_character_, length(x))
x_unknown <- character(0)
note_if_more_than_one_found <- function(found, index, from_text) {
if (initial_search == TRUE & isTRUE(length(from_text) > 1)) {
message(font_blue(paste0("NOTE: more than one result was found for item ", index, ": ",
paste0(ab_name(from_text, tolower = TRUE, initial_search = FALSE), collapse = ", "))))
}
found[1L]
}
if (initial_search == TRUE) {
progress <- progress_estimated(n = length(x), n_min = 25) # start if n >= 25
on.exit(close(progress))
}
for (i in seq_len(length(x))) {
if (initial_search == TRUE) {
progress$tick()
}
if (is.na(x[i]) | is.null(x[i])) {
next
}
if (identical(x[i], "") |
# no short names:
nchar(x[i]) <= 2 |
# prevent "bacteria" from coercing to TMP, since Bacterial is a brand name of it:
identical(tolower(x[i]), "bacteria")) {
x_unknown <- c(x_unknown, x_bak[x[i] == x_bak_clean][1])
next
}
if (isTRUE(flag_multiple_results) & x[i] %like% "[ ]") {
from_text <- suppressWarnings(ab_from_text(x[i], initial_search = FALSE, translate_ab = FALSE)[[1]])
} else {
from_text <- character(0)
}
# exact AB code
found <- antibiotics[which(antibiotics$ab == x[i]), ]$ab
if (length(found) > 0) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
# exact ATC code
found <- antibiotics[which(antibiotics$atc == x[i]), ]$ab
if (length(found) > 0) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
# exact CID code
found <- antibiotics[which(antibiotics$cid == x[i]), ]$ab
if (length(found) > 0) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
# exact name
found <- antibiotics[which(toupper(antibiotics$name) == x[i]), ]$ab
if (length(found) > 0) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
# exact LOINC code
loinc_found <- unlist(lapply(antibiotics$loinc,
function(s) x[i] %in% s))
found <- antibiotics$ab[loinc_found == TRUE]
if (length(found) > 0) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
# exact synonym
synonym_found <- unlist(lapply(antibiotics$synonyms,
function(s) x[i] %in% toupper(s)))
found <- antibiotics$ab[synonym_found == TRUE]
if (length(found) > 0) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
# exact abbreviation
abbr_found <- unlist(lapply(antibiotics$abbreviations,
function(a) x[i] %in% toupper(a)))
found <- antibiotics$ab[abbr_found == TRUE]
if (length(found) > 0) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
# allow characters that resemble others, but only continue when having more than 3 characters
if (nchar(x[i]) <= 3) {
x_unknown <- c(x_unknown, x_bak[x[i] == x_bak_clean][1])
next
}
x_spelling <- x[i]
if (already_regex == FALSE) {
x_spelling <- gsub("[IY]+", "[IY]+", x_spelling)
x_spelling <- gsub("(C|K|Q|QU|S|Z|X|KS)+", "(C|K|Q|QU|S|Z|X|KS)+", x_spelling)
x_spelling <- gsub("(PH|F|V)+", "(PH|F|V)+", x_spelling)
x_spelling <- gsub("(TH|T)+", "(TH|T)+", x_spelling)
x_spelling <- gsub("A+", "A+", x_spelling)
x_spelling <- gsub("E+", "E+", x_spelling)
x_spelling <- gsub("O+", "O+", x_spelling)
# allow any ending of -in/-ine and -im/-ime
x_spelling <- gsub("(\\[IY\\]\\+(N|M)|\\[IY\\]\\+(N|M)E\\+)$", "[IY]+(N|M)E*", x_spelling)
# allow any ending of -ol/-ole
x_spelling <- gsub("(O\\+L|O\\+LE\\+)$", "O+LE*", x_spelling)
# allow any ending of -on/-one
x_spelling <- gsub("(O\\+N|O\\+NE\\+)$", "O+NE*", x_spelling)
# replace multiple same characters to single one with '+', like "ll" -> "l+"
x_spelling <- gsub("(.)\\1+", "\\1+", x_spelling)
# replace spaces and slashes with a possibility on both
x_spelling <- gsub("[ /]", "( .*|.*/)", x_spelling)
# correct for digital reading text (OCR)
x_spelling <- gsub("[NRD8B]", "[NRD8B]", x_spelling)
x_spelling <- gsub("(O|0)", "(O|0)+", x_spelling)
x_spelling <- gsub("++", "+", x_spelling, fixed = TRUE)
}
# try if name starts with it
found <- antibiotics[which(antibiotics$name %like% paste0("^", x_spelling)), ]$ab
if (length(found) > 0) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
# try if name ends with it
found <- antibiotics[which(antibiotics$name %like% paste0(x_spelling, "$")), ]$ab
if (nchar(x[i]) >= 4 & length(found) > 0) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
# and try if any synonym starts with it
synonym_found <- unlist(lapply(antibiotics$synonyms,
function(s) any(s %like% paste0("^", x_spelling))))
found <- antibiotics$ab[synonym_found == TRUE]
if (length(found) > 0) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
# INITIAL SEARCH - More uncertain results ----
if (initial_search == TRUE) {
# only run on first try
# try by removing all spaces
if (x[i] %like% " ") {
found <- suppressWarnings(as.ab(gsub(" +", "", x[i]), initial_search = FALSE))
if (length(found) > 0 & !is.na(found)) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
}
# try by removing all spaces and numbers
if (x[i] %like% " " | x[i] %like% "[0-9]") {
found <- suppressWarnings(as.ab(gsub("[ 0-9]", "", x[i]), initial_search = FALSE))
if (length(found) > 0 & !is.na(found)) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
}
# transform back from other languages and try again
x_translated <- paste(lapply(strsplit(x[i], "[^A-Z0-9 ]"),
function(y) {
for (i in seq_len(length(y))) {
y[i] <- ifelse(tolower(y[i]) %in% tolower(translations_file$replacement),
translations_file[which(tolower(translations_file$replacement) == tolower(y[i]) &
!isFALSE(translations_file$fixed)), "pattern"],
y[i])
}
y
})[[1]],
collapse = "/")
x_translated_guess <- suppressWarnings(as.ab(x_translated, initial_search = FALSE))
if (!is.na(x_translated_guess)) {
x_new[i] <- x_translated_guess
next
}
# now also try to coerce brandname combinations like "Amoxy/clavulanic acid"
x_translated <- paste(lapply(strsplit(x_translated, "[^A-Z0-9 ]"),
function(y) {
for (i in seq_len(length(y))) {
y_name <- suppressWarnings(ab_name(y[i], language = NULL, initial_search = FALSE))
y[i] <- ifelse(!is.na(y_name),
y_name,
y[i])
}
y
})[[1]],
collapse = "/")
x_translated_guess <- suppressWarnings(as.ab(x_translated, initial_search = FALSE))
if (!is.na(x_translated_guess)) {
x_new[i] <- x_translated_guess
next
}
# try by removing all trailing capitals
if (x[i] %like_case% "[a-z]+[A-Z]+$") {
found <- suppressWarnings(as.ab(gsub("[A-Z]+$", "", x[i]), initial_search = FALSE))
if (!is.na(found)) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
}
# keep only letters
found <- suppressWarnings(as.ab(gsub("[^A-Z]", "", x[i]), initial_search = FALSE))
if (!is.na(found)) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
# try from a bigger text, like from a health care record, see ?ab_from_text
# already calculated above if flag_multiple_results = TRUE
if (isTRUE(flag_multiple_results)) {
found <- from_text[1L]
} else {
found <- suppressWarnings(ab_from_text(x[i], initial_search = FALSE, translate_ab = FALSE)[[1]][1L])
}
if (!is.na(found)) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
# first 5 except for cephalosporins, then first 7 (those cephalosporins all start quite the same!)
found <- suppressWarnings(as.ab(substr(x[i], 1, 5), initial_search = FALSE))
if (!is.na(found) && !ab_group(found, initial_search = FALSE) %like% "cephalosporins") {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
found <- suppressWarnings(as.ab(substr(x[i], 1, 7), initial_search = FALSE))
if (!is.na(found)) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
# make all consonants facultative
search_str <- gsub("([BCDFGHJKLMNPQRSTVWXZ])", "\\1*", x[i])
found <- suppressWarnings(as.ab(search_str, initial_search = FALSE, already_regex = TRUE))
# keep at least 4 normal characters
if (nchar(gsub(".\\*", "", search_str)) < 4) {
found <- NA
}
if (!is.na(found)) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
# make all vowels facultative
search_str <- gsub("([AEIOUY])", "\\1*", x[i])
found <- suppressWarnings(as.ab(search_str, initial_search = FALSE, already_regex = TRUE))
# keep at least 5 normal characters
if (nchar(gsub(".\\*", "", search_str)) < 5) {
found <- NA
}
if (!is.na(found)) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
# allow misspelling of vowels
x_spelling <- gsub("A+", "[AEIOU]+", x_spelling, fixed = TRUE)
x_spelling <- gsub("E+", "[AEIOU]+", x_spelling, fixed = TRUE)
x_spelling <- gsub("I+", "[AEIOU]+", x_spelling, fixed = TRUE)
x_spelling <- gsub("O+", "[AEIOU]+", x_spelling, fixed = TRUE)
x_spelling <- gsub("U+", "[AEIOU]+", x_spelling, fixed = TRUE)
found <- suppressWarnings(as.ab(x_spelling, initial_search = FALSE, already_regex = TRUE))
if (!is.na(found)) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
# try with switched character, like "mreopenem"
for (j in seq_len(nchar(x[i]))) {
x_switched <- paste0(
# beginning part:
substr(x[i], 1, j - 1),
# here is the switching of 2 characters:
substr(x[i], j + 1, j + 1),
substr(x[i], j, j),
# ending part:
substr(x[i], j + 2, nchar(x[i])))
found <- suppressWarnings(as.ab(x_switched, initial_search = FALSE))
if (!is.na(found)) {
break
}
}
if (!is.na(found)) {
x_new[i] <- found[1L]
next
}
} # end of initial_search = TRUE
# not found
x_unknown <- c(x_unknown, x_bak[x[i] == x_bak_clean][1])
}
if (initial_search == TRUE) {
close(progress)
}
# take failed ATC codes apart from rest
x_unknown_ATCs <- x_unknown[x_unknown %like% "[A-Z][0-9][0-9][A-Z][A-Z][0-9][0-9]"]
x_unknown <- x_unknown[!x_unknown %in% x_unknown_ATCs]
if (length(x_unknown_ATCs) > 0) {
warning("These ATC codes are not (yet) in the antibiotics data set: ",
paste('"', sort(unique(x_unknown_ATCs)), '"', sep = "", collapse = ", "),
".",
call. = FALSE)
}
if (length(x_unknown) > 0) {
warning("These values could not be coerced to a valid antimicrobial ID: ",
paste('"', sort(unique(x_unknown)), '"', sep = "", collapse = ", "),
".",
call. = FALSE)
}
x_result <- data.frame(x = x_bak_clean, stringsAsFactors = FALSE) %>%
left_join(data.frame(x = x, x_new = x_new, stringsAsFactors = FALSE), by = "x") %>%
pull(x_new)
if (length(x_result) == 0) {
x_result <- NA_character_
}
structure(.Data = x_result,
class = c("ab", "character"))
}
#' @rdname as.ab
#' @export
is.ab <- function(x) {
inherits(x, "ab")
}
#' @method print ab
#' @export
#' @noRd
print.ab <- function(x, ...) {
cat("Class <ab>\n")
print(as.character(x), quote = FALSE)
}
#' @method as.data.frame ab
#' @export
#' @noRd
as.data.frame.ab <- function(x, ...) {
nm <- deparse1(substitute(x))
if (!"nm" %in% names(list(...))) {
as.data.frame.vector(as.ab(x), ..., nm = nm)
} else {
as.data.frame.vector(as.ab(x), ...)
}
}
#' @method [ ab
#' @export
#' @noRd
"[.ab" <- function(x, ...) {
y <- NextMethod()
attributes(y) <- attributes(x)
y
}
#' @method [[ ab
#' @export
#' @noRd
"[[.ab" <- function(x, ...) {
y <- NextMethod()
attributes(y) <- attributes(x)
y
}
#' @method [<- ab
#' @export
#' @noRd
"[<-.ab" <- function(i, j, ..., value) {
y <- NextMethod()
attributes(y) <- attributes(i)
class_integrity_check(y, "antimicrobial code", antibiotics$ab)
}
#' @method [[<- ab
#' @export
#' @noRd
"[[<-.ab" <- function(i, j, ..., value) {
y <- NextMethod()
attributes(y) <- attributes(i)
class_integrity_check(y, "antimicrobial code", antibiotics$ab)
}
#' @method c ab
#' @export
#' @noRd
c.ab <- function(x, ...) {
y <- NextMethod()
attributes(y) <- attributes(x)
class_integrity_check(y, "antimicrobial code", antibiotics$ab)
}
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