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04_create_repeated_cv_folds.R
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04_create_repeated_cv_folds.R
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#### Master Script 4: Resampling of GCS data for classification ####
#
# Shubhayu Bhattacharyay, Matthew Wang, Eshan Joshi
# University of Cambridge
# Johns Hopkins University
# email address: [email protected]
#
### Contents:
# I. Initialization
# II. Isolate and save candidate (coincide with accelerometry) GCS observations
# III. Partition data with stratified, repeated cross-validation
### I. Initialization
## Import necessary packages
library(tidyverse)
library(readxl)
library(caret)
## Load neurological assessment data
gcs.data <- read.csv('../clinical_data/neurological_assessments.csv')
## Load temporal clinical metadata
patient.temporal.data <- read.csv('../clinical_data/patient_temporal_info.csv')
## Load patient outcome information for GOSE-stratification
patient.outcomes <-read.csv('../clinical_data/patient_outcomes.csv')
## Set accelerometry observation window parameters
obs.windows.hours <- c(seq(0.05,0.5,by = .05),1:3,seq(6,18,by = 3),24) #in hours
## Set parameters for repeated cross-validation
NUM.REPEATS <- 5
NUM.FOLDS <- 5
### II. Isolate and save candidate (coincide with accelerometry) GCS observations:
obs.gcs.data <- vector(mode = "list")
for (curr.obs.window in obs.windows.hours){
curr.obs.window.df <- as.data.frame(matrix(nrow = 0,ncol=8))
for (curr.UPI in patient.temporal.data$UPI){
curr.UPI.start.hour <- patient.temporal.data$HoursFromICUAdmissionAccelRecordingStart[patient.temporal.data$UPI == curr.UPI]
curr.UPI.end.hour <- patient.temporal.data$HoursFromICUAdmissionAccelRecordingEnd[patient.temporal.data$UPI == curr.UPI]
curr.gcs.data <- gcs.data %>%
filter(UPI == curr.UPI,
HoursFromICUAdmission <= curr.UPI.end.hour,
HoursFromICUAdmission >= curr.UPI.start.hour + curr.obs.window)
curr.obs.window.df <- rbind(curr.obs.window.df,curr.gcs.data)
}
curr.obs.window.df <- curr.obs.window.df %>% drop_na(GCSm)
obs.gcs.data[[sprintf('%05.2f',curr.obs.window)]] <- curr.obs.window.df
}
### III. Partition data with stratified, repeated cross-validation
dir.create('../validation_sampling',showWarnings = F)
for (curr.obs.idx in 1:length(obs.gcs.data)){
# Get median GCSm of each patient and add GOSE score at discharge
grouped.curr.obs.window.df <- obs.gcs.data[[curr.obs.idx]] %>%
group_by(UPI) %>%
summarise(GCSm = floor(median(GCSm))) %>%
left_join(patient.outcomes %>% select(UPI,GOSEDischarge,GOSE12Months),by='UPI')
# Use `caret` package to create stratified repeated cross-validation folds
curr.obs.window.GCSm.fold.idx <- createMultiFolds(grouped.curr.obs.window.df$GCSm, k = NUM.FOLDS, times = NUM.REPEATS)
curr.obs.window.GOSE.fold.idx <- createMultiFolds(grouped.curr.obs.window.df$GOSEDischarge, k = NUM.FOLDS, times = NUM.REPEATS)
# Isolate cases within the current observation window with non-missing GOSE outcomes at 12 months
grouped.12m.curr.obs.window.df <- grouped.curr.obs.window.df %>% drop_na(GOSE12Months)
# Use `caret` package to create stratified repeated cross-validation folds for 12-month outcomes
curr.obs.window.GOSE.12m.fold.idx <- createMultiFolds(grouped.12m.curr.obs.window.df$GOSE12Months, k = NUM.FOLDS, times = NUM.REPEATS)
# Create and store repeated cross-validation splits for GCSm detection
curr.obs.window.GCSm.fold.df <- as.data.frame(matrix(ncol = 11,nrow = 0))
for (fold.idx in 1:length(curr.obs.window.GCSm.fold.idx)){
curr.fold <- as.integer(str_match(names(curr.obs.window.GCSm.fold.idx)[fold.idx], "Fold\\s*(.*?)\\s*.Rep")[,2])
curr.repeat <- as.integer(sub(".*Rep", "", names(curr.obs.window.GCSm.fold.idx)[fold.idx]))
train.UPI <- grouped.curr.obs.window.df$UPI[curr.obs.window.GCSm.fold.idx[[fold.idx]]]
train.df <- obs.gcs.data[[curr.obs.idx]] %>%
filter(UPI %in% train.UPI) %>%
mutate(Fold = curr.fold,
Repeat = curr.repeat,
Split = 'Train')
test.df <- obs.gcs.data[[curr.obs.idx]] %>%
filter(!UPI %in% train.UPI) %>%
mutate(Fold = curr.fold,
Repeat = curr.repeat,
Split = 'Test')
curr.obs.window.GCSm.fold.df <- rbind(curr.obs.window.GCSm.fold.df,train.df,test.df)
}
# Get current observation window length and save current GCSm splits information
curr.obs.window.hours <- names(obs.gcs.data)[curr.obs.idx]
write.csv(curr.obs.window.GCSm.fold.df,paste0('../validation_sampling/',curr.obs.window.hours,'_h_GCSm_folds.csv'),row.names = F)
# Create and store repeated cross-validation splits for GOSE prediction at discharge
curr.obs.window.GOSE.fold.df <- as.data.frame(matrix(ncol = 11,nrow = 0))
for (fold.idx in 1:length(curr.obs.window.GOSE.fold.idx)){
curr.fold <- as.integer(str_match(names(curr.obs.window.GOSE.fold.idx)[fold.idx], "Fold\\s*(.*?)\\s*.Rep")[,2])
curr.repeat <- as.integer(sub(".*Rep", "", names(curr.obs.window.GOSE.fold.idx)[fold.idx]))
train.UPI <- grouped.curr.obs.window.df$UPI[curr.obs.window.GOSE.fold.idx[[fold.idx]]]
train.df <- obs.gcs.data[[curr.obs.idx]] %>%
filter(UPI %in% train.UPI) %>%
mutate(Fold = curr.fold,
Repeat = curr.repeat,
Split = 'Train')
test.df <- obs.gcs.data[[curr.obs.idx]] %>%
filter(!UPI %in% train.UPI) %>%
mutate(Fold = curr.fold,
Repeat = curr.repeat,
Split = 'Test')
curr.obs.window.GOSE.fold.df <- rbind(curr.obs.window.GOSE.fold.df,train.df,test.df)
}
# Get current observation window length and save current GOSE splits information
curr.obs.window.hours <- names(obs.gcs.data)[curr.obs.idx]
write.csv(curr.obs.window.GOSE.fold.df,paste0('../validation_sampling/',curr.obs.window.hours,'_h_GOSE_folds.csv'),row.names = F)
# Create and store repeated cross-validation splits for GOSE prediction at 12 months
curr.obs.window.GOSE.12m.fold.df <- as.data.frame(matrix(ncol = 11,nrow = 0))
for (fold.idx in 1:length(curr.obs.window.GOSE.12m.fold.idx)){
curr.fold <- as.integer(str_match(names(curr.obs.window.GOSE.12m.fold.idx)[fold.idx], "Fold\\s*(.*?)\\s*.Rep")[,2])
curr.repeat <- as.integer(sub(".*Rep", "", names(curr.obs.window.GOSE.12m.fold.idx)[fold.idx]))
train.UPI <- grouped.12m.curr.obs.window.df$UPI[curr.obs.window.GOSE.12m.fold.idx[[fold.idx]]]
test.UPI <- grouped.12m.curr.obs.window.df$UPI[-curr.obs.window.GOSE.12m.fold.idx[[fold.idx]]]
train.df <- obs.gcs.data[[curr.obs.idx]] %>%
filter(UPI %in% train.UPI) %>%
mutate(Fold = curr.fold,
Repeat = curr.repeat,
Split = 'Train')
test.df <- obs.gcs.data[[curr.obs.idx]] %>%
filter(UPI %in% test.UPI) %>%
mutate(Fold = curr.fold,
Repeat = curr.repeat,
Split = 'Test')
curr.obs.window.GOSE.12m.fold.df <- rbind(curr.obs.window.GOSE.12m.fold.df,train.df,test.df)
}
# Get current observation window length and save current GOSE splits information
curr.obs.window.hours <- names(obs.gcs.data)[curr.obs.idx]
write.csv(curr.obs.window.GOSE.12m.fold.df,paste0('../validation_sampling/',curr.obs.window.hours,'_h_GOSE12m_folds.csv'),row.names = F)
}