R/long_stayers.R

In MLDataR: Collection of Machine Learning Datasets for Supervised Machine Learning

#' Long stayers dataset
#' @description classification dataset of long staying patients.
#' Contains patients who have been registered as an inpatient for longer than 7 days length of stay \url{https://www.england.nhs.uk/south/wp-content/uploads/sites/6/2016/12/rig-reviewing-stranded-patients-hospital.pdf}.
#' @docType data
#' @keywords long stay patient stranded NHS
#' @format A data frame with 768 rows and 9 variables:
#' \describe{
#'   \item{stranded.label}{binary classification label indicating whether \strong{stranded = 1} or \strong{not stranded=0}}
#'   \item{age}{age of the patient}
#'   \item{care.home.referral}{flag indicating whether referred from a private care home - \strong{1=Care Home Referral} and \strong{0=Not a care home referral}}
#'   \item{medicallysafe}{flag indicating whether they are medically safe for discharge - \strong{1=Medically safe} and \strong{0=Not medically safe}}
#'   \item{hcop}{flag indicating health care for older person triage - \strong{1=Yes triaged from HCOP} and \strong{0=Triaged from different department}}
#'   \item{mental_health_care}{flag indicating whether they require mental health care - \strong{1=MH assistance needed} and \strong{0=No history of mental health}}
#'   \item{periods_of_previous_care}{Count of the number of times they have been in hospital in last 12 months}
#'   \item{admit_date}{date the patient was admitted as an inpatient}
#'   \item{frailty_index}{indicates the type of frailty - nominal variable}
#' }
#' @source Prepared, acquired and adatped by Gary Hutson \email{hutsons-hacks@outlook.com}, Dec-2021. Synthetic data, based off live patient data from various NHS secondary health care trusts.
#' @examples
#' library(dplyr)
#' library(ggplot2)
#' library(caret)
#' library(rsample)
#' library(varhandle)

#' data("long_stayers")
#' glimpse(long_stayers)
#' # Examine class imbalance
#' prop.table(table(long_stayers$stranded.label))
#' # Feature engineering
#' long_stayers <- long_stayers %>%
#' dplyr::mutate(stranded.label=factor(stranded.label)) %>%
#'  dplyr::select(everything(), -c(admit_date))
#'  # Feature encoding
#'  cats <- select_if(long_stayers, is.character)
#'  cat_dummy <- varhandle::to.dummy(cats$frailty_index, "frail_ind")
#' #Converts the frailty index column to dummy encoding and sets a column called "frail_ind" prefix
#'cat_dummy <- cat_dummy %>%
#'  as.data.frame() %>%
#'  dplyr::select(-frail_ind.No_index_item) #Drop the field of interest
#'long_stayers <- long_stayers %>%
#'  dplyr::select(-frailty_index) %>%
#'  bind_cols(cat_dummy) %>% na.omit(.)
#' # Split the data
#' split <- rsample::initial_split(long_stayers, prop = 3/4)
#' train <- rsample::training(split)
#' test <- rsample::testing(split)
#' set.seed(123)
#' glm_class_mod <- caret::train(factor(stranded.label) ~ ., data = train,
#'                              method = "glm")
#' print(glm_class_mod)
#' # Predict the probabilities
#' preds <- predict(glm_class_mod, newdata = test) # Predict class
#' pred_prob <- predict(glm_class_mod, newdata = test, type="prob") #Predict probs
#'
#'predicted <- data.frame(preds, pred_prob)
#' test <- test %>%
#'  bind_cols(predicted) %>%
#'  dplyr::rename(pred_class=preds)
#' #Evaluate with ConfusionTableR
#' library(ConfusionTableR)
#' cm <- ConfusionTableR::binary_class_cm(test$stranded.label, test$pred_class, positive="Stranded")
#' cm$record_level_cm
#' # Visualise odds ration
#' library(OddsPlotty)

#' plotty <- OddsPlotty::odds_plot(glm_class_mod$finalModel,
#'                                title = "Odds Plot ",
#'                                subtitle = "Showing odds of patient stranded",
#'                                point_col = "#00f2ff",
#'                                error_bar_colour = "black",
#'                                point_size = .5,
#'                                error_bar_width = .8,
#'                                h_line_color = "red")
#' print(plotty)

"long_stayers"