long_stayers: Long stayers dataset

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 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

Source

Prepared, acquired and adatped by Gary Hutson [e-mail], 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)

MLDataR documentation built on Oct. 3, 2022, 5:08 p.m.