Numerical range filter
The numerical range filter can only be applied on variables. We envisaged three different cases for the numerical ranges – values that are impossible, e.g. negative heart rate; possible but unlikely, e.g. heart rate of 200; within a normal range. The filter will label all these scenarios using “red”, “amber”, “green” respectively. The definition of these ranges can be configured by users based on their judgement and the purpose of research. Note, from “red” to “green”, the next range must be a subset of the previous range.
In the following section, we would like to apply a range filter to heart rate by modifying the previous YAML configuration file.
conf <- "NIHR_HIC_ICU_0108:
shortName: h_rate
dataItem: Heart rate
range:
labels:
red: (0, 300)
amber: (11, 150]
green: (50, 100]
apply: drop_entry
NIHR_HIC_ICU_0112:
shortName: bp_sys_a
dataItem: Systolic Arterial blood pressure - Art BPSystolic Arterial blood pressure
NIHR_HIC_ICU_0093:
shortName: sex
category:
M: male
F: female
m: male
f: female
"
conf <- yaml.load(conf)
tb <- create_cctable(ccd, conf, freq=1)
tb$filter_range("amber") # chose only the entry with amber
tb$apply_filters() # apply the filter to the clean table
Now let’s see the effect on the cleaned data tclean
cptb <- rbind(cbind(tb$torigin, data="origin"),
cbind(tb$tclean, data="clean"))
ggplot(cptb, aes(x=time, y=NIHR_HIC_ICU_0108, color=data)) +
geom_point(size=1.5) + facet_wrap(~episode_id, scales="free_x")
In the case of changing the filter range from amber to green,
#tb$reset() # reset the all the filters first.
tb$filter_range("green")
tb$apply_filters()
cptb <- rbind(cbind(tb$torigin, data="origin"),
cbind(tb$tclean, data="clean"))
ggplot(cptb, aes(x=time, y=NIHR_HIC_ICU_0108, color=data)) +
geom_point(size=1.5) + facet_wrap(~episode_id, scales="free_x")
Categorical data filter
The purpose of categorical data filter is to remove the unexpected categorical data. We can extend the previous configuration file as such,
conf <- "NIHR_HIC_ICU_0108:
shortName: h_rate
dataItem: Heart rate
NIHR_HIC_ICU_0112:
shortName: bp_sys_a
dataItem: Systolic Arterial blood pressure - Art BPSystolic Arterial blood pressure
NIHR_HIC_ICU_0093:
shortName: sex
category:
levels:
M: male
F: female
m: male
f: female
apply: drop_entry
"
conf <- yaml.load(conf)
# Try to modify the original data
tb$torigin$NIHR_HIC_ICU_0093[1] <- "ERROR"
tb$reload_conf(conf) # change configuration file
tb$filter_categories()
tb$apply_filters()
There is one error gender introduced in the sex field. After the filtering process, the error entry is substitute by NA.
unique(tb$torigin$NIHR_HIC_ICU_0093)
## [1] "ERROR" "F" "M"
unique(tb$tclean$NIHR_HIC_ICU_0093)
## [1] NA "F" "M"
Missingness filter
In some cases, we wish to exclude episodes where the data is too scarce. There are three components in the missingness filter. In the following example, we arbitrarily name the filter “daily”. We gave 24 hours interval and 70% accepting rate. It is to say in any 24 hours interval, if the heart rate missing rate is higher than 30%, we will exclude the entire episode. Note, the unit labels: daily: 24 number of rows instead of hours. It represents 24 hours because the cadence of the ccTable is 1 hour.
conf <- "NIHR_HIC_ICU_0108:
shortName: h_rate
dataItem: Heart rate
missingness:
labels:
daily: 24
accept_2d:
daily: 70
apply: drop_episode
NIHR_HIC_ICU_0112:
shortName: bp_sys_a
dataItem: Systolic Arterial blood pressure - Art BPSystolic Arterial blood pressure
NIHR_HIC_ICU_0093:
shortName: sex
"
conf <- yaml.load(conf)
tb$reload_conf(conf) # change configuration file
tb$filter_missingness()
tb$apply_filters()
# episodes in the original data table
unique(paste(tb$torigin$site, tb$torigin$episode_id))
## [1] "pseudo_site 1" "pseudo_site 10" "pseudo_site 11" "pseudo_site 12"
## [5] "pseudo_site 13" "pseudo_site 14" "pseudo_site 15" "pseudo_site 16"
## [9] "pseudo_site 17" "pseudo_site 18" "pseudo_site 19" "pseudo_site 2"
## [13] "pseudo_site 20" "pseudo_site 21" "pseudo_site 22" "pseudo_site 23"
## [17] "pseudo_site 24" "pseudo_site 25" "pseudo_site 26" "pseudo_site 27"
## [21] "pseudo_site 28" "pseudo_site 29" "pseudo_site 3" "pseudo_site 30"
## [25] "pseudo_site 4" "pseudo_site 5" "pseudo_site 6" "pseudo_site 7"
## [29] "pseudo_site 8" "pseudo_site 9"
# episodes in the cleaned data table
unique(paste(tb$tclean$site, tb$tclean$episode_id))
## [1] "pseudo_site 10" "pseudo_site 11" "pseudo_site 12" "pseudo_site 13"
## [5] "pseudo_site 14" "pseudo_site 15" "pseudo_site 16" "pseudo_site 17"
## [9] "pseudo_site 18" "pseudo_site 19" "pseudo_site 2" "pseudo_site 20"
## [13] "pseudo_site 21" "pseudo_site 22" "pseudo_site 23" "pseudo_site 24"
## [17] "pseudo_site 25" "pseudo_site 26" "pseudo_site 27" "pseudo_site 28"
## [21] "pseudo_site 29" "pseudo_site 3" "pseudo_site 30" "pseudo_site 4"
## [25] "pseudo_site 5" "pseudo_site 6" "pseudo_site 7" "pseudo_site 8"
## [29] "pseudo_site 9"
Nodata filter
Similarly, we can setup the no data filter as following. Here it means, drop the entire episode if no hear rate data is found.
NIHR_HIC_ICU_0108:
shortName: h_rate
dataItem: Heart rate
no_data:
apply: drop_episode
NIHR_HIC_ICU_0112:
shortName: bp_sys_a
dataItem: Systolic Arterial blood pressure - Art BPSystolic Arterial blood pressure
NIHR_HIC_ICU_0093:
shortName: sex
Run all filters together
To wrap up, we can put all the above stated filter configurations together in the YAML file and run the filter together.
conf <- "NIHR_HIC_ICU_0108:
shortName: h_rate
dataItem: Heart rate
range:
labels:
red: (0, 300)
amber: (11, 150]
green: (50, 100]
apply: drop_entry
missingness:
labels:
daily: 24
accept_2d:
daily: 70
apply: drop_episode
nodata:
apply: drop_episode
NIHR_HIC_ICU_0112:
shortName: bp_sys_a
dataItem: Systolic Arterial blood pressure - Art BPSystolic Arterial blood pressure
NIHR_HIC_ICU_0093:
shortName: sex
category:
levels:
M: male
F: female
m: male
f: female
apply: drop_entry
"
conf <- yaml.load(conf)
# Method 1
tb <- create_cctable(ccd, conf, freq=1)
tb$filter_range("amber")
tb$filter_missingness()
tb$filter_nodata()
tb$filter_categories()
tb$apply_filters()
tb$reset() # reset
# Method 2
#tb$clean()
Imputation
We provide the impute() to interpolate the missing data. For each missing value, the interpolation will be only based on the nearby values which are specified by lead and lag arguments. lead suggests the number of previous values and lag suggests the number of later values. The corresponding time will be related to the freq you set for the ccTable, e.g. lead: 2 means previous 4 hours when freq=0.5. One can also set the fun to determine the interpolation function. The imputation step usually should be carried out after filtering, otherwise imputation will take values that are out of range its into account.
One needs to be always careful when impute the data to make the best trade-off between usefulness and correctness. The interpolation methods should be carried out wisely based on the characteristics of the variable. A good overview of how to deal with the missing data can be found (Salagodo et al. 2016) If you are not sure about the characteristics of the variable, we would suggest you to keep the window size small and use median as the interpolation function.
# Initialise the simulated ccRecord
hr <- c(rep(80, 10), rep(NA, 10), rep(90, 10), NA, NA, rep(90, 10), rep(NA, 10), 180, NA, NA,
rep(90, 10), 180, NA, 0, NA, NA, rep(60, 10))
# hr <- hr + runif(length(hr)) * 15 # adding noise if needed.
data <- data.frame(time=as.numeric(seq(hr)), item2d=hr)
rec <- ccRecord()+new.episode(list(NIHR_HIC_ICU_0108=data))
# Prepare the plotting function
library(data.table)
plot_imputation <- function() {
cptb <- data.table(episode_id=as.integer(tb$torigin$episode_id),
time=tb$torigin$time, origin=tb$torigin$NIHR_HIC_ICU_0108,
clean=tb$tclean$NIHR_HIC_ICU_0108)
ggplot(cptb, aes(x=time)) +
geom_point(size=5, shape=16, aes(y=origin), colour="red") +
geom_point(size=2, aes(y=clean)) +
geom_line(aes(y=clean)) +
scale_x_continuous(minor_breaks = seq(length(hr)))+
theme(panel.grid.minor = element_line(colour="grey", size=0.5),
panel.grid.major = element_line(colour="grey", size=0.5))
}
Example 1: median interpolation with a window [-2, 2]
# mock the configuration YAML
conf <- "NIHR_HIC_ICU_0108:
shortName: h_rate
dataItem: Heart rate
missingness:
impute:
lead: 2 # 2 previous values
lag: 2 # 2 later values
fun: median # missing value filled by the median of 2 previous and 2 later values.
nodata:
apply: drop_episode
"
conf <- yaml.load(conf)
tb <- create_cctable(rec, conf, freq=1)
tb$imputation()
plot_imputation()
Example 2: increase the window size to [-10, 10] We can increase the window size to fill more data,
conf <- "NIHR_HIC_ICU_0108:
shortName: h_rate
dataItem: Heart rate
missingness:
impute:
lead: 10
lag: 10
fun: median
nodata:
apply: drop_episode
"
rec <- ccRecord()+new.episode(list(NIHR_HIC_ICU_0108=data))
conf <- yaml.load(conf)
tb <- create_cctable(rec, conf, freq=1)
tb$imputation()
plot_imputation()
Example 3: use mean as the interpolation function.
conf <- "NIHR_HIC_ICU_0108:
shortName: h_rate
dataItem: Heart rate
missingness:
impute:
lead: 10
lag: 10
fun: mean
nodata:
apply: drop_episode
"
rec <- ccRecord()+new.episode(list(NIHR_HIC_ICU_0108=data))
conf <- yaml.load(conf)
tb <- create_cctable(rec, conf, freq=1)
tb$imputation()
plot_imputation()
