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load_data.R
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load_data.R
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# This file is part of CEOsys Recommendation Checker.
#
# Copyright (c) 2021 CEOsys project team <https://covid-evidenz.de>.
#
# CEOsys Recommendation Checker is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# CEOsys Recommendation Checker is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with CEOsys Recommendation Checker. If not, see <https://www.gnu.org/licenses/>.
library(httr)
library(dotenv)
library(stringr)
library(readr)
library(dplyr)
library(lubridate)
OFFLINE <- nchar(Sys.getenv('OFFLINE')) > 0
OFFLINE_PATH = 'offline-data/'
base_url <- "http://localhost:8001"
# base_url <- Sys.getenv("UI_BACKEND_SERVER")
rec_map <- list(
"recommendation_url" = c(
"https://www.netzwerk-universitaetsmedizin.de/fhir/codex-celida/guideline/covid19-inpatient-therapy/recommendation/no-therapeutic-anticoagulation",
"https://www.netzwerk-universitaetsmedizin.de/fhir/codex-celida/guideline/sepsis/recommendation/ventilation-plan-ards-tidal-volume",
"https://www.netzwerk-universitaetsmedizin.de/fhir/codex-celida/guideline/covid19-inpatient-therapy/recommendation/ventilation-plan-ards-tidal-volume",
"https://www.netzwerk-universitaetsmedizin.de/fhir/codex-celida/guideline/covid19-inpatient-therapy/recommendation/covid19-ventilation-plan-ards-peep",
"https://www.netzwerk-universitaetsmedizin.de/fhir/codex-celida/guideline/covid19-inpatient-therapy/recommendation/prophylactic-anticoagulation",
"https://www.netzwerk-universitaetsmedizin.de/fhir/codex-celida/guideline/covid19-inpatient-therapy/recommendation/therapeutic-anticoagulation",
"https://www.netzwerk-universitaetsmedizin.de/fhir/codex-celida/guideline/covid19-inpatient-therapy/recommendation/covid19-abdominal-positioning-ards"
),
"short" = c("No ACT", "Sepsis/Tidal", "C19/Tidal", "PEEP", "p-ACT", "t-ACT", "Proning")
) %>% as_tibble()
COLUMN_SUFFIXES <- c(".data", ".days", ".comment")
read_offline_data <- function(name) {
return(read_csv(paste0(OFFLINE_PATH, name, '.csv'), show_col_types = FALSE))
}
expand_colnames <- function(colnames, suffixes = COLUMN_SUFFIXES) {
#' Expand Column Names with Custom Suffixes
#'
#' This function takes a character vector of column names and a character vector
#' of suffixes, and expands the column names by appending each suffix to each
#' column name. The expanded column names are returned in a specific order,
#' with each group of suffixes appearing together in the sequence.
#'
#' @param colnames A character vector containing the original column names
#' @param suffixes A character vector containing the custom suffixes to be
#' appended to the column names
#'
#' @return A character vector containing the expanded column names in the
#' specified order, with each group of suffixes appearing together
#' in the sequence
#'
#' @examples
#' ## Expand a vector of column names with custom suffixes
#' original_colnames <- c("A", "B")
#' custom_suffixes <- c(".data", ".days", ".comment")
#' expanded_colnames <- expand_colnames(original_colnames, custom_suffixes)
#' print(expanded_colnames)
ordered_colnames <- unname(unlist(lapply(colnames, function(x) sapply(suffixes, function(s) paste0(x, s)))))
return(ordered_colnames)
}
load_recommendations <- function() {
#' Load Recommendations
#'
#' This function retrieves a list of recommendations from a specified endpoint and converts the response into a tibble data frame.
#' The response is joined with a pre-defined mapping data frame 'rec_map', by the column 'recommendation_url'.
#'
#' @return A tibble data frame containing the recommendations with added columns from the mapping data frame.
#' @export
if(!OFFLINE) {
req <- GET(paste0(base_url, "/recommendation/list"))
recommendations <- jsonlite::fromJSON(content(req, as = "text", encoding = "UTF-8")) %>%
as_tibble() %>%
inner_join(rec_map, by = "recommendation_url")
} else {
recommendations <- read_offline_data("recommendations")
}
#write_csv(recommendations, "data-new/recommendations.csv")
return(recommendations)
}
recommendations <- load_recommendations()
summarize_category <- function(categories) {
#' Summarize categories
#'
#' This function takes a character vector of categories and returns a summarized representation based on the presence of 'population_intervention', 'population', or 'intervention' categories.
#'
#' @param categories A character vector of categories.
#' @return A character string representation of the summarized category: "PI" for 'population_intervention', "P" for 'population', "I" for 'intervention', and "o" for other categories.
#' @examples
#' categories <- c("population_intervention", "other", "another")
#' summarize_category(categories) # returns "PI"
#'
if ("population_intervention" %in% categories) {
return("PI")
} else if ("population" %in% categories) {
return("P")
} else if ("intervention" %in% categories) {
return("I")
} else {
return("o")
}
}
load_patient_list <- function(selected_recommendation_urls, start_datetime, end_datetime) {
#' Load a list of patients based on selected recommendations and time period
#'
#' This function loads a list of patients based on selected recommendations and time period.
#'
#' @param selected_recommendation_urls character vector of recommendation urls to be used
#' @param start_datetime Datetime for the start of the time period
#' @param end_datetime Datetime for the end of the time period
#'
#' @return A list containing patients data in tibble format and run_ids in tibble format.
#'
#' @examples
#' result <- load_patient_list(c("recommendation1","recommendation2"), "2021-01-01", "2021-01-31")
#' patients <- result$patients
#' run_ids <- result$run_id
#'
patients <- tibble()
rec_short_names <- rec_map$short
if (is.null(selected_recommendation_urls)) {
return(patients)
}
if (!OFFLINE) {
for (recommendation_url in selected_recommendation_urls) {
req <- GET(paste0(base_url, "/patient/list/?recommendation_url=", URLencode(recommendation_url), "&start_datetime=", URLencode(as.character(start_datetime)), "&end_datetime=", URLencode(as.character(end_datetime))))
data <- jsonlite::fromJSON(content(req, as = "text", encoding = "UTF-8"))
run_id <- data$run_id
pat_data <- data$data %>%
as_tibble() %>%
mutate(run_id = run_id, url = recommendation_url)
patients <- bind_rows(patients, pat_data)
}
} else {
patients <- read_offline_data("patients")
}
#write_csv(patients, "data-new/patients.csv")
run_ids <- patients %>% distinct(run_id, url)
if (nrow(patients) > 0) {
patients <- patients %>%
# make up a ward TODO: should be real ward
mutate(Ward = as.factor(sprintf("ITS %02d", (person_id %% 3) + 1))) %>%
inner_join(rec_map %>% rename(url = recommendation_url), by = "url") %>%
pivot_wider(id_cols = c("person_id", "Ward"), names_from = "short", values_from = "cohort_category", values_fn = summarize_category) %>%
arrange(person_id) %>%
mutate(Patient = person_id)
} else {
# no patients received - return a valid tibble but without rows
patients <- bind_cols(
tibble(Patient = character(), person_id = character(), Ward = character(), .rows = 0),
tibble(!!!rec_short_names, .rows = 0, .name_repair = ~rec_short_names)
)
}
# make up comment data
t_comment <- patients %>%
mutate_at(all_of(rec_short_names), ~ runif(nrow(patients)) > 0.5)
# make up day data
generate_random_strings <- function(x, n) {
random_strings <- sapply(1:x, function(i) {
random_digits <- sample(0:2, n, replace = TRUE)
random_string <- paste0(random_digits, collapse = "")
return(random_string)
})
return(random_strings)
}
n_days <- 10
t_days <- patients %>%
mutate_at(vars(all_of(rec_short_names)), ~ generate_random_strings(length(.), n_days))
# determine percentage data
percentage <- function(input_vector) {
result <- sapply(input_vector, function(input_string) {
split <- strsplit(input_string, "")[[1]]
# Count the occurrences of 1 and 2
count_1 <- sum(split == "1")
count_2 <- sum(split == "2")
# Divide the number of 2s by the sum of the number of 1s and 2s
result <- round(count_2 / (count_1 + count_2) * 100, 2)
return(result)
})
return(result)
}
t_percentage <- t_days %>% mutate(across(all_of(rec_short_names), percentage))
# combine days, percentage and comment data into a single tibble
t_percentage <- t_percentage %>% rename_with(~ paste0(., ".data"))
t_days <- t_days %>% rename_with(~ paste0(., ".days"))
t_comment <- t_comment %>% rename_with(~ paste0(., ".comment"))
# Combine tibbles
combined_tibble <- bind_cols(t_percentage, t_days, t_comment)
# Generate the desired order of column names
expanded_colnames <- expand_colnames(rec_short_names)
# Reorder columns
ordered_tibble <- combined_tibble %>% select(all_of(expanded_colnames))
result <- bind_cols(patients %>% select(-all_of(rec_short_names)), ordered_tibble)
return(list(patients = result, run_id = run_ids))
}
# t_days<-load_patient_list(rec_map$recommendation_url, start_datetime="2023-01-01", end_datetime="2023-04-03")
load_recommendation_variables <- function(recommendation_url) {
#' Load Recommendation Variables
#'
#' The function `load_recommendation_variables()` retrieves the criteria information for a specified recommendation URL.
#'
#' @param recommendation_url Character string of the recommendation URL
#'
#' @return A tibble with columns `type`, `variable_name`, and `criterion_name`
#'
#' @examples
#' criteria <- load_recommendation_variables("www.example.com/recommendation/1234")
if(!OFFLINE) {
req <- GET(paste0(base_url, "/recommendation/criteria/?recommendation_url=", URLencode(recommendation_url)))
data <- jsonlite::fromJSON(content(req, as = "text", encoding = "UTF-8"))
criteria <- data$criterion %>%
as_tibble() %>%
rename(type = cohort_category, variable_name = concept_name, criterion_name = unique_name)
} else {
criteria <- read_offline_data("criteria") %>% filter({{recommendation_url}} == recommendation_url)
}
#library(cli)
#rec_hash = hash_md5(recommendation_url)
#write_csv(criteria %>% mutate(recommendation_url=recommendation_url), glue("data-new/criteria-{rec_hash}.csv"))
return(criteria)
}
load_data <- function(person_id, run_id, criterion_name, start_date, end_date) {
#' Load patient data based on person_id, run_id, and criterion_name
#'
#' @param person_id character string identifying a person
#' @param run_id character string identifying a run
#' @param criterion_name character string identifying a criterion
#' @param min_dt datetime of the beginning of the observation window
#' @param max_dt datetime of the end of the observation window
#'
#' @return a tibble with patient data, arranged by datetime. Columns may include:
#' - `datetime`: start datetime of the patient data
#' - `end_datetime`: end datetime of the patient data (defaults to `datetime` if not present)
#' - `value`: value of the patient data, renamed from `value_as_number` or `drug_dose_as_number` if present
#'
#' @examples
#' patientdata <- load_data("12345", "run1", "criterion_a")
#'
#' @export
#'
if (is.null(person_id) | length(person_id) == 0) {
return(NULL)
}
if(OFFLINE) {
tbl <- generate_tibble(person_id, start_date, end_date, criterion_name)
return(tbl)
}
req <- GET(paste0(base_url, "/patient/data/?person_id=", URLencode(as.character(person_id)), "&run_id=", URLencode(as.character(run_id)), "&criterion_name=", URLencode(criterion_name)))
if (req$status_code != 200) {
stop("Error encountered during load_data")
}
patientdata <- jsonlite::fromJSON(content(req, as = "text", encoding = "UTF-8")) %>% as_tibble()
patientdata <- patientdata %>%
rename(datetime = start_datetime) %>%
arrange(datetime)
if (("value_as_number" %in% names(patientdata))) {
patientdata <- patientdata %>%
rename(value = value_as_number)
} else if ("drug_dose_as_number" %in% names(patientdata)) {
patientdata <- patientdata %>%
rename(value = drug_dose_as_number)
} else {
patientdata <- patientdata %>%
mutate(value = TRUE)
}
if (!("end_datetime" %in% names(patientdata))) {
patientdata <- patientdata %>%
mutate(end_datetime = datetime)
}
if (nrow(patientdata) > 0) {
patientdata <- patientdata %>%
mutate(end_datetime = coalesce(end_datetime, datetime)) %>%
mutate(datetime = parse_datetime(datetime)) %>%
mutate(end_datetime = parse_datetime(end_datetime))
}
#write_csv(patientdata, glue("data-new/patientdata-{person_id}.csv"))
return(patientdata)
}
generate_tibbleX <- function(person_id, start_date, end_date, criterion_name){
# seed the random number generator
md5_hash = digest::digest(criterion_name, "md5")
set.seed(as.integer(person_id) + strtoi(substr(md5_hash, 1, 5), 16))
# initialize the parameter_concept_id to 0
parameter_concept_id <- 0
# determine the type of the criterion and number of entries
if (any(startsWith(criterion_name, c("Measurement_", "TidalVolumePerIdealBodyWeight_", "ConceptCriterion_")))) {
num_entries <- sample(24:24*60, 1) # measurements: once per day to once per hour
} else if (startsWith(criterion_name, "DrugExposure_")){
num_entries <- sample(0:2, 1) # drug exposures: 0 to 2 times per day
} else if (any(startsWith(criterion_name, c("ConditionOccurrence_", "ProcedureOccurrence_", "VisitOccurrence_")))) {
num_entries <- sample(0:1, 1) # occurrences: 0 to 1 times per day
} else (
stop("No entry found for criterion_name = ", criterion_name)
)
# generate datetime and end_datetime
datetimes <- sample(seq(as.POSIXct(start_date), as.POSIXct(end_date), by="min"), num_entries)
end_datetimes <- datetimes + dminutes(sample(0:60, num_entries, replace = TRUE)) # random duration for occurrences
# generate values
if (any(startsWith(criterion_name, c("Measurement_", "TidalVolumePerIdealBodyWeight_", "ConceptCriterion_")))) {
if (grepl("aPTT", criterion_name)) values <- runif(num_entries, 20, 40) # adjust as per clinically plausible values
else if (grepl("Tidal-volume", criterion_name)) values <- runif(num_entries, 5, 10)
else if (grepl("D-dimer", criterion_name)) values <- runif(num_entries, 0, 0.5)
else if (grepl("PEEP", criterion_name)) values <- runif(num_entries, 5, 20)
else if (grepl("Body-weight", criterion_name)) values <- runif(num_entries, 50, 100)
else if (grepl("Inhaled-oxygen-concentration", criterion_name)) values <- runif(num_entries, 21, 100)
else if (grepl("Horowitz-index", criterion_name)) values <- runif(num_entries, 200, 500)
else if (grepl("Pressure-max", criterion_name)) values <- runif(num_entries, 10, 30)
} else if (startsWith(criterion_name, "DrugExposure_")){
values <- runif(num_entries, 0.1, 1.0) # adjust as per clinically plausible values
} else if (any(startsWith(criterion_name, c("ConditionOccurrence_", "ProcedureOccurrence_", "VisitOccurrence_")))) {
values <- rep(1, num_entries)
}
# create a tibble
df <- tibble(
person_id = rep(person_id, num_entries),
parameter_concept_id = rep(parameter_concept_id, num_entries),
datetime = datetimes,
end_datetime = end_datetimes,
value = values
)
return(df)
}
generate_tibble <- function(person_id, start_date, end_date, criterion_name){
# seed the random number generator
md5_hash = digest::digest(criterion_name, "md5")
set.seed(as.integer(person_id) + strtoi(substr(md5_hash, 1, 5), 16))
# initialize the parameter_concept_id to 0
parameter_concept_id <- 0
# calculate the number of days in the observation period
num_days <- as.integer(difftime(end_date, start_date, units = "days"))
type_range <- TRUE
# determine the type of the criterion and number of entries per day
if (any(startsWith(criterion_name, c("Measurement_", "TidalVolumePerIdealBodyWeight_", "ConceptCriterion_")))) {
type_range <- FALSE
if (grepl("aPTT", criterion_name)) entries_per_day <- sample(4:6, 1) # aPTT: 4-6 times per day
else if (grepl("Tidal-volume", criterion_name)) entries_per_day <- sample(24:24*4, 1) # Tidal volume: 1-2 times per day
else if (grepl("D-dimer", criterion_name)) entries_per_day <- sample(1:2, 1) # D-dimer: 1-2 times per day
else if (grepl("PEEP", criterion_name)) entries_per_day <- sample(24:24*4, 1) # PEEP: 1-4 times per day
else if (grepl("Body-weight", criterion_name)) entries_per_day <- 1/num_days # Body weight: once per day
else if (grepl("Inhaled-oxygen-concentration", criterion_name)) entries_per_day <- sample(24:24*4, 1) # Inhaled oxygen concentration: 1-2 times per day
else if (grepl("Horowitz-index", criterion_name)) entries_per_day <- sample(24:24*4, 1) # Horowitz index: 1-2 times per day
else if (grepl("Pressure-max", criterion_name)) entries_per_day <- sample(24:24*4, 1) # Maximal pressure during respiration: 1-4 times per day
} else if (startsWith(criterion_name, "DrugExposure_")){
entries_per_day <- sample(0:2, 1) # drug exposures: 0 to 2 times per day
} else if (any(startsWith(criterion_name, c("ProcedureOccurrence_")))) {
entries_per_day <- sample(0:1, 1) # occurrences: 0 to 1 times per day
} else if (any(startsWith(criterion_name, c("ConditionOccurrence_", "VisitOccurrence_")))) {
entries_per_day <- sample(0:1, 1) / num_days # occurrences: 0 to 1 times per stay
} else {
stop("No entry found for criterion_name = ", criterion_name)
}
# calculate the total number of entries
num_entries <- num_days * entries_per_day
# generate datetime and end_datetime
datetimes <- sample(seq(as.POSIXct(start_date), as.POSIXct(end_date), by="min"), num_entries)
if(num_entries == 1) {
sample_range <- 300:3000
} else {
sample_range <- 5:720
}
end_datetimes <- datetimes
if (type_range) {
end_datetimes <- end_datetimes + as.difftime(sample(sample_range, num_entries, replace = TRUE), units = "mins") # random duration for occurrences
}
# generate values
if (any(startsWith(criterion_name, c("Measurement_", "TidalVolumePerIdealBodyWeight_", "ConceptCriterion_")))) {
if (grepl("aPTT", criterion_name)) values <- runif(num_entries, 20, 40) # adjust as per clinically plausible values
else if (grepl("Tidal-volume", criterion_name)) values <- runif(num_entries, 5, 10)
else if (grepl("D-dimer", criterion_name)) values <- runif(num_entries, 0, 0.5)
else if (grepl("PEEP", criterion_name)) values <- runif(num_entries, 5, 20)
else if (grepl("Body-weight", criterion_name)) values <- runif(num_entries, 50, 100)
else if (grepl("Inhaled-oxygen-concentration", criterion_name)) values <- runif(num_entries, 21, 100)
else if (grepl("Horowitz-index", criterion_name)) values <- runif(num_entries, 200, 500)
else if (grepl("Pressure-max", criterion_name)) values <- runif(num_entries, 10, 30)
} else if (startsWith(criterion_name, "DrugExposure_")){
values <- runif(num_entries, 0.1, 1.0) # adjust as per clinically plausible values
} else if (any(startsWith(criterion_name, c("ConditionOccurrence_", "ProcedureOccurrence_", "VisitOccurrence_")))) {
values <- rep(1, num_entries)
}
# create a tibble
df <- tibble(
person_id = rep(person_id, num_entries),
parameter_concept_id = rep(parameter_concept_id, num_entries),
datetime = datetimes,
end_datetime = end_datetimes,
value = values
) %>% arrange(datetime)
return(df)
}