This tutorial introduces the various expected formats for medication dosing data.
for most recent version, install from GitHub
remotes::install_github('choileena/EHR/EHR')introduce data formats
input to run_Build_PK_IV/run_Build_PK
flow <- data.frame(mod_id=c(1,1,2,2,2),
mod_id_visit=c(46723,46723,84935,84935,84935),
record.date=c("07/05/2019 5:25","07/05/2019 6:01",
"09/04/2020 3:21", "09/04/2020 4:39",
"09/04/2020 5:32"),
Final.Weight=c(6.75,6.75,4.5,4.5,4.5),
Final.Rate=c(rep("1 mcg/kg/hr",2),
rep("0.5 mcg/kg/hr",3)),
Final.Units=c("3.375","6.5",
"2.25","2.25","2.25")
)
flow[,'Perform.Date'] <- pkdata::parse_dates(flow[,'record.date'])
flow[,'pd'] <- pkdata::parse_dates(flow[,'record.date'], tz = 'UTC')
flow[,'unit'] <- sub('.*[ ]', '', flow[,'Final.Rate'])
flow[,'rate'] <- as.numeric(sub('([0-9.]+).*', '\\1', flow[,'Final.Rate']))
mar <- data.frame(mod_id=rep(1,5),
Date=rep("2019-07-05",5),
Time=c("07:12","07:31","08:47","09:16","10:22"),
`med:mDrug`=c("Fakedrug2","Fakedrug1","Fakedrug2",
"Fakedrug3","Fakedrug4"),
`med:dosage`=c("30 mg","0.5 mcg","1 mg",
"20 mg","3 mcg/kg/min"),
`med:route`=rep("IV",5),
`med:given`=rep("Given",5),
check.names=FALSE
)
mar[,'dt'] <- as.POSIXct(paste(mar$Date, mar$Time), tz = 'UTC')
mar[,'dosage'] <- c(30,0.5,1,20,3)
mar[,'unit'] <- c('mg','mcg','mg','mg','mcg/kg/min')input to run_Build_PK_Oral/run_Build_PK
erx_data <- data.frame(GRID=paste0("ID",c(1,1,2,2,2,2)),
MED_NAME=c("fakedrug","fakedrug","fakedrug","Brandname","fakedrug","fakedrug"),
RX_DOSE=c(1,2,1,'2 tabs',1,'1+1.5+1'),
FREQUENCY=c(rep("bid",3),"qam","bid","brkfst,lunch,dinner"),
ENTRY_DATE=c("2018-02-15","2018-03-14","2017-07-01","2017-07-01","2017-09-15","2017-11-01"),
STRENGTH_AMOUNT=c("100","100","200","100mg","100","100"),
DESCRIPTION=c("fakedrug 100 mg tablet","fakedrug 100 mg tablet",
"fakedrug 200 mg tablet (also known as brandname)",
"Brandname 100mg tablet", "fakedrug 100 mg tablet",
"fakedrug 100 mg tablet")
)input to run_Build_PK_IV/run_Build_PK
doseData <- structure(list(id = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1),
dt = c("2020-01-01 08:00", "2020-01-02 08:00", "2020-01-02 20:00",
"2020-01-02 20:00", "2020-01-03 08:00", "2020-01-05 08:00", "2020-01-05 08:00",
"2020-01-05 08:00", "2020-01-05 20:00", "2020-01-06 08:00", "2020-01-06 08:00",
"2020-01-06 20:00", "2020-01-07 20:00", "2020-01-07 20:00"),
dose = c(30, 10, 20, 30, 10, 10, 20, 30, 10, 20, 10, 10, 20, 20),
dur = c(3600, 7200, 1800, 3600, 3600, 7200, 1800, 3600, 7200, 1800, 1800, 1800, 1800, 3600)),
class = "data.frame", row.names = c(NA, -14L)
)set.seed(250)
n <- 10
isOP <- rbinom(n, 1, 0.8)
id <- sample(150, n, replace = TRUE)
rxopt <- c(
'amoxicillin 875 MG / clavulanate 125 MG Oral Tablet [Augmentin]',
'amoxicillin 80 MG/ML Oral Suspension [Amoxil]',
'amoxicillin 500 MG Oral Capsule',
'amoxicillin 120 MG/ML / clavulanate 8.58 MG/ML Oral Suspension'
)
pickrx <- sample(rxopt, n, replace = TRUE, prob = c(.4,.35,.15,.1))
rxid <- match(pickrx, rxopt)
dose_u <- character(n)
dose_u[rxid==1] <- 'tablet'
dose_u[rxid==3] <- 'mg'
dose_u[rxid==2] <- sample(c('mg/kg','mg/kg/day','mg'), sum(rxid==2), replace = TRUE)
dose_u[rxid==4] <- sample(c('mg/kg','mg/kg/day','mg','mL'), sum(rxid==4), replace = TRUE)
dose_n <- numeric(n)
dose_n[rxid==1] <- 1
dose_n[rxid==3] <- sample(c(500,1000,2000), sum(rxid==3), replace = TRUE)
du <- match(dose_u, c('mg/kg','mg/kg/day','mg','mL'))
dose_n[rxid==2 & du==1] <- sample(c(45, 50, 10, 25), sum(rxid==2 & du==1), replace = TRUE)
dose_n[rxid==2 & du==2] <- sample(c(90, 50, 80, 25), sum(rxid==2 & du==2), replace = TRUE)
dose_n[rxid==2 & du==3] <- sample(c(500, 1000, 800, 400), sum(rxid==2 & du==3), replace = TRUE)
dose_n[rxid==4 & du==1] <- 435
dose_n[rxid==4 & du==2] <- sample(c(90, 80, 40), sum(rxid==4 & du==2), replace = TRUE)
dose_n[rxid==4 & du==3] <- sample(c(875, 600), sum(rxid==4 & du==3), replace = TRUE)
dose_n[rxid==4 & du==4] <- 5
qnt <- numeric(n)
qnt[rxid %in% c(1,3)] <- sample(c(1:30, 60, 90, 180), sum(rxid %in% c(1,3)), replace = TRUE)
qnt[rxid %in% c(2,4)] <- sample(600, sum(rxid %in% c(2,4)), replace = TRUE)
time <- as.POSIXct(sample(100, n, replace = TRUE) * 86400, origin = '2020-01-01', tz = 'UTC')
time <- as.POSIXct(format(time, '%Y-%m-%d %H:%M'))
endtime <- rep(as.POSIXct(NA), n)
endtime[isOP==1] <- time[isOP==1] + sample(0:10, sum(isOP==1), replace = TRUE) * 86400
drug <- data.frame(
MRN=id,
DRUG_EXPOSURE_ID=NA,
PERSON_ID=id,
DRUG_CONCEPT_ID=rxid,
CONCEPT_DRUG_NAME=pickrx,
DRUG_TYPE_CONCEPT_ID=c(38000180,38000177)[isOP+1],
DRUG_EXPOSURE_START_DATETIME=time,
DRUG_EXPOSURE_END_DATETIME=endtime,
STOP_REASON=NA,
REFILLS=0,
QUANTITY=qnt,
DAYS_SUPPLY=NA,
ROUTE_SOURCE_VALUE='oral',
DOSE_UNIT_SOURCE_VALUE=dose_u,
X_DOC_TYPE=c('EPIC IP ADMIN','EPIC OP ORDER')[isOP+1],
X_DOC_STYPE=NA,
X_DOSE=dose_n,
X_DRUG_FORM='tablet',
X_STRENGTH=c('875-125 mg','400 mg/5 mL','500 mg','600-42.9 mg/5 mL')[rxid],
X_FREQUENCY=sample(c('daily','Every 12 hours scheduled'), n, replace = TRUE),
X_QUANTITY_UNIT=c('tablet','mL','capsule','mL')[rxid],
X_DURATION=NA
)
uid <- sort(unique(id))
demo <- data.frame(MRN=uid, birthDate=as.Date(sample(20*365.25, length(uid), replace = TRUE), origin = '2000-01-01'))
wgts <- data.frame(
id=uid,
dt=as.Date(sample(365, length(uid), replace = TRUE), origin = '2020-01-01'),
val=sample(10:20, length(uid), replace = TRUE)
)references
If you see mistakes or want to suggest changes, please create an issue on the source repository.