item2d <- c("h_rate", "bp_m_a", "bp_sys_a", "lactate_abg", "bilirubin", "fluid_balance_h", "pao2_abg") new.config <- function(items) { conf <- list() for (i in items) conf[[stname2code(i)]] <- list() conf } cctb <- create.cctable(ccd, conf=new.config(item2d), freq=1)$torigin suppressWarnings(library(pander)) panderOptions("digits", 2) panderOptions("table.split.table", Inf) demg <- sql.demographic.table(ccd)
dp <- total.data.point(ccd) np <- max(ccd@infotb$pid, na.rm=TRUE)
This database contains r ccd@nepisodes
episode data from
r length(s[s!="NA"])
sites. Based on the NHS numbers and
PAS numbers, we can identify r np
unique patients, among which the earliest
admission is r min(ccd@infotb$t_admission, na.rm=TRUE)
and the latest discharge time is
r max(ccd@infotb$t_discharge, na.rm=TRUE)
. There are r dp
total data points found in the current database, which makes in average
r round(dp/np)
per unique patient.
pander(as.data.frame(site.info()[, 1:3], style="rmarkdown"))
fs <- file.summary(ccd) pander(as.data.frame(fs[, c("File", "Number of Episode", "Sites"), with=FALSE]))
xml.file.duration.plot(ccd)
xml.site.duration.plot(ccd)
\newpage
table1(demg, c("SEX", "ETHNIC", "LOCA", "DIS", "HDIS"))
demographic.data.completeness(demg)
This is the section to show the completeness of some key time-wise data, e.g. physiological data. The completeness of data is measured by sample period. The sample period $P$ is defined as the ratio of the total admission hour $T$ to the number of valid hours $D$ where data can be found. $$P = \frac{T}{D}$$
samplerate2d(cctb)
\newpage
demg.distribution(demg, c("HCM", "apache_score"))
physio.distribution(cctb, item2d)
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