MLDataR (version 1.0.1)

long_stayers: 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 https://www.england.nhs.uk/south/wp-content/uploads/sites/6/2016/12/rig-reviewing-stranded-patients-hospital.pdf.

Usage

long_stayers

Format

A data frame with 768 rows and 9 variables:

stranded.label

binary classification label indicating whether stranded = 1 or not stranded=0

age

age of the patient

care.home.referral

flag indicating whether referred from a private care home - 1=Care Home Referral and 0=Not a care home referral

medicallysafe

flag indicating whether they are medically safe for discharge - 1=Medically safe and 0=Not medically safe

hcop

flag indicating health care for older person triage - 1=Yes triaged from HCOP and 0=Triaged from different department

mental_health_care

flag indicating whether they require mental health care - 1=MH assistance needed and 0=No history of mental health

periods_of_previous_care

Count of the number of times they have been in hospital in last 12 months

admit_date

date the patient was admitted as an inpatient

frailty_index

indicates the type of frailty - nominal variable

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)