Version:	0.4-11
Date:	2022-12-15
Title:	Electronic Health Record (EHR) Data Processing and Analysis Tool
Maintainer:	Leena Choi <leena.choi@vanderbilt.edu>
Description:	Process and analyze electronic health record (EHR) data. The 'EHR' package provides modules to perform diverse medication-related studies using data from EHR databases. Especially, the package includes modules to perform pharmacokinetic/pharmacodynamic (PK/PD) analyses using EHRs, as outlined in Choi, Beck, McNeer, Weeks, Williams, James, Niu, Abou-Khalil, Birdwell, Roden, Stein, Bejan, Denny, and Van Driest (2020) <doi:10.1002/cpt.1787>. Additional modules will be added in future. In addition, this package provides various functions useful to perform Phenome Wide Association Study (PheWAS) to explore associations between drug exposure and phenotypes obtained from EHR data, as outlined in Choi, Carroll, Beck, Mosley, Roden, Denny, and Van Driest (2018) <doi:10.1093/bioinformatics/bty306>.
Depends:	R (≥ 2.10)
License:	GPL (≥ 3)
URL:	https://choileena.github.io/
Imports:	stats, utils, data.table, methods, lubridate, pkdata
Suggests:	glmnet, logistf, medExtractR (≥ 0.4.1), knitr, rmarkdown, ggplot2, markdown
NeedsCompilation:	no
RoxygenNote:	7.1.1
VignetteBuilder:	knitr
Packaged:	2022-12-19 16:07:42 UTC; beckca
Author:	Leena Choi [aut, cre], Cole Beck [aut], Hannah Weeks [aut], Elizabeth McNeer [aut], Nathan James [aut], Michael Williams [aut]
Repository:	CRAN
Date/Publication:	2022-12-19 18:20:02 UTC

Electronic Health Record (EHR) Data Processing and Analysis Tool

Description

The 'EHR' package provides modules to perform diverse medication-related studies using data from EHR databases.

Details

Package functionality:

• Process and analyze Electronic Health Record (EHR) data.

• Implement modules to perform diverse medication-related studies using data from EHR databases. Especially, the package includes modules to perform pharmacokinetic/pharmacodynamic (PK/PD) analyses using EHRs, as outlined in Choi et al. (2020).

• Implement three statistical methods for Phenome Wide Association Study (PheWAS). Contingency tables for many binary outcomes (e.g., phenotypes) and a binary covariate (e.g., drug exposure) can be efficiently generated by zeroOneTable, and three commonly used statistical methods to analyze data for PheWAS are implement by analysisPheWAS.

Author(s)

Maintainer: Leena Choi leena.choi@vanderbilt.edu (ORCID)

Authors:

• Cole Beck cole.beck@vumc.org (ORCID)

• Hannah Weeks hannah.l.weeks@vanderbilt.edu (ORCID)

• Elizabeth McNeer elizabeth.mcneer@vumc.org

• Nathan James nathan.t.james@vanderbilt.edu (ORCID)

• Michael Williams michael.l.williams@vanderbilt.edu

References

1. Development of a system for postmarketing population pharmacokinetic and pharmacodynamic studies using real-world data from electronic health records.
   Choi L, Beck C, McNeer E, Weeks HL, Williams ML, James NT, Niu X, Abou-Khalil BW, Birdwell KA, Roden DM, Stein CM, Bejan CA, Denny JC, Van Driest SL.
   Clin Pharmacol Ther. 2020 Apr;107(4):934-943. doi: 10.1002/cpt.1787.

2. Evaluating statistical approaches to leverage large clinical datasets for uncovering therapeutic and adverse medication effects.
   Choi L, Carroll RJ, Beck C, Mosley JD, Roden DM, Denny JC, Van Driest SL.
   Bioinformatics. 2018 Sep 1;34(17):2988-2996. doi: 10.1093/bioinformatics/bty306.

See Also

Useful links:

• https://choileena.github.io/

Firth's penalized-likelihood logistic regression with more decimal places of p-value than logistf function in the R package ‘logistf’

Description

Adapted from logistf in the R package ‘logistf’, this is the same as logistf except that it provides more decimal places of p-value that would be useful for Genome-Wide Association Study (GWAS) or Phenome Wide Association Study (PheWAS).

Usage

Logistf(
  formula = attr(data, "formula"),
  data = sys.parent(),
  pl = TRUE,
  alpha = 0.05,
  control,
  plcontrol,
  firth = TRUE,
  init,
  weights,
  plconf = NULL,
  dataout = TRUE,
  ...
)

Arguments

Value

same as logistf except for providing more decimal places of p-value.

Author(s)

Leena Choi leena.choi@vanderbilt.edu and Cole Beck cole.beck@vumc.org

References

same as those provided in the R package ‘logistf’.

Examples

data(dataPheWAS)
fit <- Logistf(X264.3 ~ exposure + age + race + gender, data=dd)
summary(fit)

Add Lastdose Data

Description

Add lastdose data to data set from the buildDose process.

Usage

addLastDose(buildData, lastdoseData)

Arguments

Details

Lastdose is a datetime string associated with dose data. Information on time of last dose can be extracted within the extractMed function (i.e., medExtractR) using the argument lastdose=TRUE. Raw extracted times should first be processed using the processLastDose function to convert to datetime format before providing to addLastDose. This function then combines the processed last dose times with output from the buildDose process by file name to pair last dose times with dosing regimens based on position. Alternatively, the user can provide their own table of lastdose data. In this case, with position information absent, the lastdose data should be restricted to one unique last dose time per unique patient ID-date identifier.

In the case where lastdoseData is output from processLastDose, it is possible to have more than one extracted last dose time. In this case, rules are applied to determine which time should be kept. First, we give preference to an explicit time expression (e.g., "10:30pm") over a duration expression (e.g., "14 hour level"). Then, we pair last dose times with drug regimens based on minimum distance between last dose time start position and drug name start position.

See EHR Vignette for Extract-Med and Pro-Med-NLP for details.

Value

a data.frame with the ‘lastdose’ column added.

Examples

# Get build data
data(tac_mxr_parsed)
# don't combine lastdose at this stage
tac_build <- buildDose(tac_mxr_parsed, preserve = 'lastdose')
# Get processed last dose data
tac_mxr <- read.csv(system.file("examples", "tac_mxr.csv", package = "EHR"))
data(tac_metadata)
data(tac_lab)
ld_data <- processLastDose(tac_mxr, tac_metadata, tac_lab)

addLastDose(tac_build, ld_data)

Statistical Analysis for PheWAS

Description

Implement three commonly used statistical methods to analyze data for Phenome Wide Association Study (PheWAS)

Usage

analysisPheWAS(
  method = c("firth", "glm", "lr"),
  adjust = c("PS", "demo", "PS.demo", "none"),
  Exposure,
  PS,
  demographics,
  phenotypes,
  data
)

Arguments

Details

Implements three commonly used statistical methods to analyze the associations between exposure (e.g., drug exposure, genotypes) and various phenotypes in PheWAS. Firth's penalized-likelihood logistic regression is the default method to avoid the problem of separation in logistic regression, which is often a problem when analyzing sparse binary outcomes and exposure. Logistic regression with likelihood ratio test and conventional logistic regression with Wald test can be also performed.

Value

Author(s)

Leena Choi leena.choi@vanderbilt.edu and Cole Beck cole.beck@vumc.org

Examples

## use small datasets to run this example
data(dataPheWASsmall)
## make dd.base with subset of covariates from baseline data (dd.baseline.small)
## or select covariates with upper code as shown below
upper.code.list <- unique(sub("[.][^.]*(.).*", "", colnames(dd.baseline.small)) )
upper.code.list <- intersect(upper.code.list, colnames(dd.baseline.small))
dd.base <- dd.baseline.small[, upper.code.list]
## perform regularized logistic regression to obtain propensity score (PS) 
## to adjust for potential confounders at baseline
phenos <- setdiff(colnames(dd.base), c('id', 'exposure'))
data.x <- as.matrix(dd.base[, phenos])
glmnet.fit <- glmnet::cv.glmnet(x=data.x, y=dd.base[,'exposure'],
                                family="binomial", standardize=TRUE,
                                alpha=0.1)
dd.base$PS <- c(predict(glmnet.fit, data.x, s='lambda.min'))
data.ps <- dd.base[,c('id', 'PS')]
dd.all.ps <- merge(data.ps, dd.small, by='id')  
demographics <- c('age', 'race', 'gender')
phenotypeList <- setdiff(colnames(dd.small), c('id','exposure','age','race','gender'))
## run with a subset of phenotypeList to get quicker results
phenotypeList.sub <- sample(phenotypeList, 5)
results.sub <- analysisPheWAS(method='firth', adjust='PS', Exposure='exposure',
                              PS='PS', demographics=demographics, 
                              phenotypes=phenotypeList.sub, data=dd.all.ps)
## run with the full list of phenotype outcomes (i.e., phenotypeList)

        results <- analysisPheWAS(method='firth', adjust='PS',Exposure='exposure',
                          PS='PS', demographics=demographics,
                          phenotypes=phenotypeList, data=dd.all.ps) 
         

Internal functions for buildDose process

Description

These internal functions aid the buildDose function and process.

Details

tstrsplit2: a wrapper for the tstrsplit function in data.table with some additional formatting

convert: formats final results that are returned in buildDose, including separating the extracted expression from its start and stop position

entVal: isolates the extracted expression from a string of the format "extracted expression::start position::stop position"

entStart: isolates the start position from a string of the format "extracted expression::start position::stop position"

entStop: isolates the stop position from a string of the format "extracted expression::start position::stop position"

remainder: performs some formatting for paths containing NA values

getCombosIx: forms the individual paths within the getPaths function

getPaths: builds paths from entity pairings using other helper function getCombosIx

getOpt: takes in the possible paths as well as the cost computations from getCosts, combines costs at the path level using the helper functions remainder and getPaths, and returns the path with the minimum cost

getCosts: computes the distance for each path based on the distance method specified in buildDose

findOptPath: uses the helper functions getCosts and getOpt to determine the optimal path based on the selected distance method

stdRoute: standardize the "route" entity. This function is also used in the collapseDose function and process.

grp: groups drug entities based on a particular anchor point (e.g. strength or dose)

anchorByStrength: forms subgroups of entities by anchoring to strength. This is done within existing groups which are anchored by drug name

combineGroups: determines how to form groups of the entities based on the complexity of the extraction (i.e., number of extractions for each entity)

makeCombos: takes all extracted entities and puts them into groups based on position and minimum cost of paths. This is the highest level function within buildDose; it calls other helper functions including combineGroups and anchorByStrength.

findRareValues: find rare values for each column in a data.frame; rare defaults to less than two percent.

Combine Dose Data

Description

Output from parse process is taken and converted into a wide format, grouping drug entity information together based on various steps and rules.

Usage

buildDose(
  dat,
  dn = NULL,
  preserve = NULL,
  dist_method,
  na_penalty,
  neg_penalty,
  greedy_threshold,
  checkForRare = FALSE
)

Arguments

Details

The buildDose function takes as its main input (dat), a data.table object that is the output of a parse process function (parseMedExtractR, parseMedXN, parseMedEx, or parseCLAMP). Broadly, the parsed extractions are grouped together to form wide, more complete drug regimen information. This reformatting facilitates calculation of dose given intake and daily dose in the collapseDose process.

The process of creating this output is broken down into multiple steps:

1. Removing rows for any drugs not of interest. Drugs of interest are specified with the dn argument.

2. Determining whether extractions are "simple" (only one drug mention and at most one extraction per entity) or complex. Complex cases can be more straightforward if they contain at most one extraction per entity, or require a pairing algorithm to determine the best pairing if there are multiple extractions for one or more entities.

3. Drug entities are anchored by drug name mention within the parse process. For complex cases, drug entities are further grouped together anchored at each strength (and dose with medExtractR) extraction.

4. For strength groups with multiple extractions for at least one entity, these groups go through a path searching algorithm, which computes the cost for each path (based on a chosen distance method) and chooses the path with the lowest cost.

5. The chosen paths for each strength group are returned as the final pairings. If route is unique within a strength group, it is standardized and added to all entries for that strength group.

The user can specify additional arguments including:

• dist_method: The distance method is the metric used to determine which entity path is the most likely to be correct based on minimum cost.

• na_penalty: NA penalties are incurred when extractions are paired with nothing (i.e., an NA), requiring that entities be sufficiently far apart from one another before being left unpaired.

• neg_penalty: When working with dose amount (DA) and frequency/intake time (FIT), it is much more common for the ordering to be DA followed by FIT. Thus, when we observe FIT followed by DA, we apply a negative penalty to make such pairings less likely.

• greedy threshold: When there are many extractions from a clinical note, the number of possible combinations for paths can get exponentially large, particularly when the medication extraction natural language processing system is incorrect. The greedy threshold puts an upper bound on the number of entity pairings to prevent the function from stalling in such cases.

If none of the optional arguments are specified, then the buildDose process uses the default option values specified in the EHR package documentation. See EHR Vignette for Extract-Med and Pro-Med-NLP as well as Dose Building Using Example Vanderbilt EHR Data for details. For additional details, see McNeer, et al. 2020.

Value

A data.frame object that contains columns for filename (of the clinical note, inherited from the parse output object dat), drugname, strength, dose, route, freq, duration, and drugname_start.

Examples

data(lam_mxr_parsed)

buildDose(lam_mxr_parsed)

PK oral functions

Description

codebuild_lastdose: build PK oral with lastdose information

Usage

build_lastdose(x, first_interval_hours = 336, ldCol = NULL)

Internal functions for collapseDose process

Description

These internal functions aid the main functions used in the collapse process (collapseDose, makeDose).

Details

qrbind: fast version of rbind.data.frame

nowarnnum: converts a variable to numeric and suppresses warnings

most: returns the mode

pairDay: calculates daily dose for dose sequences

mergeAdjacent: collapses adjacent rows that have a drug name change

borrowFreqDoseSeq: imputes a frequency of 'pm' if the preceding row has a frequency of 'am' or 'noon' and there is a 'pm' elsewhere in the note

borrowVal: borrow a unique value within a drug mention

reOrder: order rows

borrowWithinDoseSeq: borrow values within a dose sequence

calcDailyDose: calculates daily dose

daySeqDups: look for duplicate dose sequence

rmDuplicates: removes redundant rows

Collapse Dose Data

Description

Splits drug data and calls makeDose to collapse at the note and date level.

Usage

collapseDose(x, noteMetaData, naFreq = "most", ...)

Arguments

Details

If different formulations of the drug (e.g., extended release) exist, they can be separated using a regular expression (e.g., ‘xr|er’). This function will call makeDose on parsed and paired medication data to calculate dose intake and daily dose and remove redundancies at the note and date level.

See EHR Vignette for Extract-Med and Pro-Med-NLP as well as Dose Building Using Example Vanderbilt EHR Data for details.

Value

A list containing two dataframes, one with the note level and one with the date level collapsed data.

Examples

data(lam_mxr_parsed)
data(lam_metadata)

lam_build_out <- buildDose(lam_mxr_parsed)

lam_collapsed <- collapseDose(lam_build_out, lam_metadata, naFreq = 'most', 'xr|er')
lam_collapsed$note # Note level collapsing
lam_collapsed$date # Date level collapsing

Data Transformation

Description

Convenience function for making small modifications to a data.frame.

Usage

dataTransformation(x, select, rename, modify)

Arguments

Value

The modified data.frame

dd

Description

Simulated outcome data example from Phenome Wide Association Study (PheWAS) that examines associations between drug exposure and various phenotypes at follow-up after the drug exposure. The dataset includes 1505 variables: subject identification number ('id'), drug exposure ('exposure'), 3 demographic variables ('age', 'race', 'gender'), and 1500 phenotypes.

Usage

data(dataPheWAS, package = 'EHR')

Format

A data frame with 10000 observations on 1505 variables.

Examples

data(dataPheWAS)

dd.baseline

Description

Simulated baseline data example from a Phenome Wide Association Study (PheWAS) obtained at baseline before drug exposure. The dataset includes 1505 variables: subject identification number ('id'), drug exposure ('exposure'), 3 demographic variables ('age', 'race', 'gender'), and 1500 phenotypes.

Usage

data(dataPheWAS, package = 'EHR')

Format

A data frame with 10000 observations on 1505 variables.

Examples

data(dataPheWAS)

dd.baseline.small

Description

A smaller subset of baseline data example, dd.baseline. The dataset includes 55 variables: subject identification number ('id'), drug exposure ('exposure'), 3 demographic variables ('age', 'race', 'gender'), and 50 phenotypes.

Usage

data(dataPheWASsmall, package = 'EHR')

Format

A data frame with 2000 observations on 55 variables.

Examples

data(dataPheWASsmall)

dd.small

Description

A smaller subset of outcome data example, 'dd'. The dataset includes 55 variables: subject identification number ('id'), drug exposure ('exposure'), 3 demographic variables ('age', 'race', 'gender'), and 50 phenotypes.

Usage

data(dataPheWASsmall, package = 'EHR')

Format

A data frame with 2000 observations on 55 variables.

Examples

data(dataPheWASsmall)

Extract medication information from clinical notes

Description

This function is an interface to the medExtractR function within the medExtractR package, and allows drug dosing information to be extracted from free-text sources, e.g., clinical notes.

Usage

extractMed(note_fn, drugnames, drgunit, windowlength, max_edit_dist = 0, ...)

Arguments

Details

Medication information, including dosing data, is often stored in free-text sources such as clinical notes. The extractMed function serves as a convenient wrapper for the medExtractR package, a natural language processing system written in R for extracting medication data. Within extractMed, the medExtractR function identifies dosing data for drug(s) of interest, specified by the drugnames argument, using rule-based and dictionary-based approaches. Relevant dosing entities include medication strength (identified using the unit argument), dose amount, dose given intake, intake time or frequency of dose, dose change keywords (e.g., 'increase' or 'decrease'), and time of last dose. After applying medExtractR to extract drug dosing information, extractMed appends the file name to results to ensure they are appropriately labeled.

See EHR Vignette for for Extract-Med and Pro-Med-NLP. For more details, see Weeks, et al. 2020.

Value

A data.frame with the extracted dosing information, labeled with file name as an identifier
Sample output:

Examples

tac_fn <- list(system.file("examples", "tacpid1_2008-06-26_note1_1.txt", package = "EHR"),
               system.file("examples", "tacpid1_2008-06-26_note2_1.txt", package = "EHR"),
               system.file("examples", "tacpid1_2008-12-16_note3_1.txt", package = "EHR"))

extractMed(tac_fn,
           drugnames = c("tacrolimus", "prograf", "tac", "tacro", "fk", "fk506"),
           drgunit = "mg",
           windowlength = 60,
           max_edit_dist = 2,
           lastdose=TRUE)

Internal functions for Extract-Med module

Description

These internal functions aid the main function used in the Extract-Med module (extractMed).

Usage

getNote(note)

Details

getNote: Takes the file name and reads in the note as a character vector

getDose: Runs medExtractR on a single clinical note (file) and labels the results output with file name

Convert Character Frequency to Numeric

Description

This function converts the frequency entity to numeric.

Usage

freqNum(x)

Arguments

Value

numeric vector

Examples

f <- stdzFreq(c('in the morning', 'four times a day', 'with meals'))
freqNum(f)

Create ID Crosswalk

Description

Link ID columns from multiple data sets. De-identified columns are created to make a crosswalk.

Usage

idCrosswalk(data, idcols, visit.id = "subject_id", uniq.id = "subject_uid")

Arguments

Details

‘visit.id’ and ‘uniq.id’ may occur multiple times, but should have a one-to-one linkage defined by at least one of the input data sets. A new visit number is generated for each repeated ‘uniq.id’.

Value

crosswalk of ID columns and their de-identified versions

Examples

demo_data <- data.frame(subj_id=c(4.1,4.2,5.1,6.1),
                        pat_id=c(14872,14872,24308,37143),
                        gender=c(1,1,0,1),
                        weight=c(34,42,28,63),
                        height=c(142,148,120,167))

conc_data <- data.frame(subj_id=rep((4:6)+0.1,each=5),
                        event=rep(1:5,times=3),
                        conc.level=15*exp(-1*rep(1:5,times=3))+rnorm(15,0,0.1))

data <- list(demo_data, conc_data)
idcols <- list(c('subj_id', 'pat_id'), 'subj_id')
idCrosswalk(data, idcols, visit.id='subj_id', uniq.id='pat_id')

Example of Metadata for Lamotrigine Data

Description

An example of the metadata needed for the processLastDose, makeDose, and collapseDose functions.

Usage

data(lam_metadata, package = 'EHR')

Format

A data frame with 5 observations on the following variables.

filename

A character vector, filename for the clinical note

pid

A character vector, patient ID associated with the filename

date

A character vector, date associated with the filename

note

A character vector, note ID associated with the filename

Examples

data(lam_metadata)

Example of Lamotrigine Output from 'parseMedExtractR'

Description

The output after running parseMedExtractR on 4 example clinical notes.

Usage

data(lam_mxr_parsed, package = 'EHR')

Format

A data frame with 10 observations on the following variables.

filename

A character vector, filename for the clinical note

drugname

A character vector, drug name extracted from the clinical note along with start and stop positions

strength

A character vector, strengths extracted from the clinical note along with start and stop positions

dose

A character vector, dose amounts extracted from the clinical note along with start and stop positions

route

A character vector, routes extracted from the clinical note along with start and stop positions

freq

A character vector, frequencies extracted from the clinical note along with start and stop positions

dosestr

A character vector, dose intakes extracted from the clinical note along with start and stop positions

dosechange

A character vector, dose change keywords extracted from the clinical note along with start and stop positions

lastdose

A character vector, last dose times extracted from the clinical note along with start and stop positions

Examples

data(lam_mxr_parsed)

Internal functions for lastdose

Description

These internal functions incorporate lastdose.

Details

matchLastDose: match lastdose data to built data

mergeLastDose: merge lastdose data into built data

getDuration: Duration - don't convert just extract number

convertAMPM: convert am/pm times

convertMornNight: if night/morning used, convert to am or pm

convertMilitary: convert military format into 12h

standardizeTime: create standardized form of time

checkTime: check if lastdose expression is a time expression in the format HH:MM:SS

checkDuration: check if lastdose expression is a duration expression (number that is not in the HH:MM:SS format)

Make Dose Data

Description

Takes parsed and paired medication data, calculates dose intake and daily dose, and removes redundant information at the note and date level.

Usage

makeDose(x, noteMetaData, naFreq = "most")

Arguments

Details

This function standardizes frequency, route, and duration entities. Dose amount, strength, and frequency entities are converted to numeric. Rows with only drug name and/or route are removed. If there are drug name changes in adjacent rows (e.g., from a generic to brand name), these rows are collapsed into one row if there are no conflicts. Missing strengths, dose amounts, frequencies, and routes are borrowed or imputed using various rules (see McNeer et al., 2020 for details). Dose given intake and daily dose are calculated. Redundancies are removed at the date and note level. If time of last dose is being used and it is unique within the level of collapsing, it is borrowed across all rows.

Value

A list containing two dataframes, one with the note level and one with the date level collapsed data.

Examples

data(lam_mxr_parsed)
data(lam_metadata)

lam_build_out <- buildDose(lam_mxr_parsed)

lam_collapsed <- makeDose(lam_build_out, lam_metadata)
lam_collapsed[[1]] # Note level collapsing
lam_collapsed[[2]] # Date level collapsing

Internal functions for EHR modules

Description

codecolflow_mod: build a comparison of potentially duplicate flow data

Details

codefindMedFlowRow_mod: find row numbers for matching ID and time

codeflowData_mod: correct flow data

codeconcData_mod: correct concentration data

codeinfusionData_mod: combine flow and MAR data; standardize hourly rate, which may or may not include weight

coderesolveDoseDups_mod: correct duplicated infusion and bolus doses

codewriteCheckData

codeprocessErx: prepare e-prescription data

codeprocessErxAddl: additional processing, checking for connected observations

codevalidateColumns: check columns are in place

coderead: generic read function

codesetDoseMar: set unit/rate/date columns

Internal functions for parse process

Description

These internal functions aid the main functions used in the parsing process (parseMedExtractR, parseMedXN, parseMedEx, parseCLAMP).

Details

medxnColonFormat: converts entity information into the form "extracted expression::start position::stop position", similar to how output is formatted by default in MedXN output.

entorder: a helper function that orders the entities by start position

medxnEntityFormat: a helper function that applies both medxnColonFormat to convert entities into the "extraction::start::stop" format and entorder to sort them. It then collapses the entities and separates the extractions with backticks

Parse CLAMP NLP Output

Description

Takes files with the raw medication extraction output generated by the CLAMP natural language processing system and converts it into a standardized format.

Usage

parseCLAMP(filename)

Arguments

Details

Output from different medication extraction systems is formatted in different ways. In order to be able to process the extracted information, we first need to convert the output from different systems into a standardized format. Extracted expressions for various drug entities (e.g., drug name, strength, frequency, etc.) each receive their own column formatted as "extracted expression::start position::stop position". If multiple expressions are extracted for the same entity, they will be separated by backticks.

CLAMP output files anchor extractions to a specific drug name extraction through semantic relations.

See EHR Vignette for Extract-Med and Pro-Med-NLP as well as Dose Building Using Example Vanderbilt EHR Data for details.

Value

A data.table object with columns for filename, drugname, strength, dose, route, and freq. The filename contains the file name corresponding to the clinical note. Each of the entity columns are of the format "extracted expression::start position::stop position".

Parse MedEx NLP Output

Description

Takes files with the raw medication extraction output generated by the MedEx natural language processing system and converts it into a standardized format.

Usage

parseMedEx(filename)

Arguments

Details

Output from different medication extraction systems is formatted in different ways. In order to be able to process the extracted information, we first need to convert the output from different systems into a standardized format. Extracted expressions for various drug entities (e.g., drug name, strength, frequency, etc.) each receive their own column formatted as "extracted expression::start position::stop position". If multiple expressions are extracted for the same entity, they will be separated by backticks.

MedEx output files anchor extractions to a specific drug name extraction.

See EHR Vignette for Extract-Med and Pro-Med-NLP as well as Dose Building Using Example Vanderbilt EHR Data for details.

Value

A data.table object with columns for filename, drugname, strength, dose, route, and freq. The filename contains the file name corresponding to the clinical note. Each of the entity columns are of the format "extracted expression::start position::stop position".

Parse medExtractR NLP Output

Description

Takes files with the raw medication extraction output generated by the medExtractR natural language processing system and converts it into a standardized format.

Usage

parseMedExtractR(filename)

Arguments

Details

Output from different medication extraction systems is formatted in different ways. In order to be able to process the extracted information, we first need to convert the output from different systems into a standardized format. Extracted expressions for various drug entities (e.g., drug name, strength, frequency, etc.) each receive their own column formatted as "extracted expression::start position::stop position". If multiple expressions are extracted for the same entity, they will be separated by backticks.

The medExtractR system returns extractions in a long table format, indicating the entity, extracted expression, and start:stop position of the extraction. To perform this initial parsing, entities are paired with the closest preceding drug name. The one exception to this is the dose change entity, which can occur before the drug name (see Weeks, et al. 2020 for details).

See EHR Vignette for Extract-Med and Pro-Med-NLP as well as Dose Building Using Example Vanderbilt EHR Data for details.

Value

A data.table object with columns for filename, drugname, strength, dose, route, freq, dosestr, dosechange and lastdose. The filename contains the file name corresponding to the clinical note. Each of the entity columns are of the format "extracted expression::start position::stop position".

Examples

mxr_output <- system.file("examples", "lam_mxr.csv", package = "EHR")
mxr_parsed <- parseMedExtractR(mxr_output)
mxr_parsed

Parse MedXN NLP Output

Description

Takes files with the raw medication extraction output generated by the MedXN natural language processing system and converts it into a standardized format.

Usage

parseMedXN(filename, begText = "^[R0-9]+_[0-9-]+_[0-9]+_")

Arguments

Details

Output from different medication extraction systems is formatted in different ways. In order to be able to process the extracted information, we first need to convert the output from different systems into a standardized format. Extracted expressions for various drug entities (e.g., drug name, strength, frequency, etc.) each receive their own column formatted as "extracted expression::start position::stop position". If multiple expressions are extracted for the same entity, they will be separated by backticks.

MedXN output files anchor extractions to a specific drug name extraction.

In MedXN output files, the results from multiple clinical notes can be combined into a single output file. The beginning of some lines of the output file can indicate when output for a new observation (or new clinical note) begins. The user should specify the argument begText to be a regular expression used to identify the lines where output for a new clinical note begins.

See EHR Vignette for Extract-Med and Pro-Med-NLP as well as Dose Building Using Example Vanderbilt EHR Data for details.

Value

A data.table object with columns for filename, drugname, strength, dose, route, freq, and duration. The filename contains the file name corresponding to the clinical note. Each of the entity columns are of the format "extracted expression::start position::stop position".

Examples

mxn_output <- system.file("examples", "lam_medxn.csv", package = "EHR")
mxn_parsed <- parseMedXN(mxn_output, begText = "^ID[0-9]+_[0-9-]+_")
mxn_parsed

Internal functions for pk analysis

Description

codetakeClosest: take value at nearest time

Details

codetakeClosestTime: find nearest time

codedaysDiff: number of days between two dates

codeminDiff: difference (in minutes) of time vector

codefixDates: add leading zeroes to date

codejoinFlowMar: rbind two data sets

codeupdateInterval_mod: replace maxint with difference between surgery time and infusion time (if smaller than maxint)

codefix_serum_time: create time variable

codemerge_by_time: impute in x by closest time in y

codemerge_inf_bolus: combine infusion and bolus into one hourly dose file

codeaddZeroDose: add rows of zero dose, after a gap

codepkdata

Process and standardize extracted last dose times

Description

This function takes last dose times extracted using the medExtractR system and processes the times into standardized datetime objects using recorded lab data where necessary. The raw output from extractMed is filtered to just the LastDose extractions. Time expressions are standardized into HH:MM:SS format based on what category they fall into (e.g., a time represented with AM/PM, 24-hour military time, etc.). When the last dose time is after 12pm, it is assumed to have been taken one day previous to the note's date. For any duration extractions (e.g. "14 hour level"), the last dose time is calculated from the labtime by extracting the appropriate number of hours. The final dataset is returned with last dose time formatted into a POSIXct variable.

Usage

processLastDose(mxrData, noteMetaData, labData)

Arguments

Details

See EHR Vignette for Extract-Med and Pro-Med-NLP for details.

Value

data.frame with identifying information (e.g., filename, etc) as well as processed and standardized last dose times as a POSIXct column

Examples

tac_mxr <- read.csv(system.file("examples", "tac_mxr.csv", package = "EHR"))
data(tac_metadata)
data(tac_lab)

processLastDose(mxrData = tac_mxr, noteMetaData = tac_metadata, labData = tac_lab)

Pull Fake/Mod ID

Description

Replace IDs with de-identified version pulled from a crosswalk.

Usage

pullFakeId(
  dat,
  xwalk,
  firstCols = NULL,
  orderBy = NULL,
  uniq.id = "subject_uid"
)

Arguments

Value

The modified data.frame

Examples

demo_data <- data.frame(subj_id=c(4.1,4.2,5.1,6.1),
                        pat_id=c(14872,14872,24308,37143),
                        gender=c(1,1,0,1),
                        weight=c(34,42,28,63),
                        height=c(142,148,120,167))

# crosswalk w/ same format as idCrosswalk() output
xwalk <- data.frame(subj_id=c(4.1,4.2,5.1,6.1),
                    pat_id=c(14872,14872,24308,37143),
                    mod_visit=c(1,2,1,1),
                    mod_id=c(1,1,2,3),
                    mod_id_visit=c(1.1,1.2,2.1,3.1))

demo_data_deident <- pullFakeId(demo_data, xwalk, 
                                firstCols = c('mod_id','mod_id_visit','mod_visit'),
                                uniq.id='pat_id')

Pull Real ID

Description

Replace de-identified IDs with identified version pulled from a crosswalk.

Usage

pullRealId(dat, xwalk = NULL, remove.mod.id = FALSE)

Arguments

Value

The modified data.frame

Examples

demo_data_deident <- data.frame(mod_id=c(1,1,2,3),
                                mod_id_visit=c(1.1,1.2,2.1,3.1),
                                mod_visit=c(1,2,1,1),
                                gender=c(1,1,0,1),
                                weight=c(34,42,28,63),
                                height=c(142,148,120,167))

# crosswalk w/ same format as idCrosswalk() output
xwalk <- data.frame(subj_id=c(4.1,4.2,5.1,6.1),
                    pat_id=c(14872,14872,24308,37143),
                    mod_visit=c(1,2,1,1),
                    mod_id=c(1,1,2,3),
                    mod_id_visit=c(1.1,1.2,2.1,3.1))

pullRealId(demo_data_deident, xwalk)
pullRealId(demo_data_deident, xwalk, remove.mod.id=TRUE)

Read and Transform

Description

Convenience function for reading in a CSV file, and making small modifications to a data.frame.

Usage

readTransform(file, ...)

Arguments

Details

If read.csv needs additional arguments (or the file is in a different format), the user should load the data first, then directly call dataTransformation.

Value

The modified data.frame

Build-PK-IV Module

Description

This module builds PK data for intravenously (IV) administered medications.

Usage

run_Build_PK_IV(
  conc,
  conc.columns = list(),
  dose,
  dose.columns = list(),
  censor = NULL,
  censor.columns = list(),
  demo.list = NULL,
  demo.columns = list(),
  lab.list = NULL,
  lab.columns = list(),
  dosePriorWindow = 7,
  labPriorWindow = 7,
  postWindow = NA,
  pk.vars = NULL,
  drugname = NULL,
  check.path = NULL,
  missdemo_fn = "-missing-demo",
  faildupbol_fn = "DuplicateBolus-",
  date.format = "%m/%d/%y %H:%M:%S",
  date.tz = "America/Chicago",
  isStrict = FALSE
)

Arguments

Details

See EHR Vignette for Structured Data.

Regarding the ‘gap’ variable in the dose dataset, if ‘gap’ is specified in ‘dose.columns’, it allows a continuous infusion given when there are missing records between infusion dosing records. For example, suppose that ‘gap’ = 60 is defined (which is typical gap size when infusion dosing is supposed to be recorded hourly for inpatients) and time between two records (i.e., gap) are greater than 1 hour (i.e., missing records). If the gap between the two records is less or equal to twice of the gap (i.e., 2*60 = 120 min), a continuous infusion is assumed until the 2nd dose record; otherwise, the first infusion is assumed to be stopped (i.e., add zero doses) after 60 min (i.e., equal to the gap size) and a new infusion (the 2nd record) starts at its recorded time.

Value

PK data set

Examples

# make fake data
set.seed(6543)

build_date <- function(x) format(seq(x, length.out=5, by="1 hour"), "%Y-%m-%d %H:%M")
dates <- unlist(lapply(rep(Sys.time(),3), build_date))

plconc <- data.frame(mod_id = rep(1:3,each=5),
                   mod_id_visit = rep(1:3,each=5)+0.1,
                   event = rep(1:5,times=3),
                   conc.level = 15*exp(-1*rep(1:5,times=3))+rnorm(15,0,0.1),
                   date.time = as.POSIXct(dates))

ivdose <- data.frame(mod_id = 1:3,
                     date.dose = substr(dates[seq(1,15,by=5)],1,10),
                     infuse.time.real = NA, infuse.time = NA, infuse.dose = NA,
                     bolus.time = as.POSIXct(dates[seq(1,15,by=5)])-300,
                     bolus.dose = 90,
                     maxint = 0L,
                     weight = 45)


run_Build_PK_IV(conc = plconc,
                conc.columns = list(id = 'mod_id', datetime = 'date.time',
                  druglevel = 'conc.level', idvisit = 'mod_id_visit'),
                dose = ivdose,
                dose.columns = list(id = 'mod_id', date = 'date.dose',
                  bolusDatetime = 'bolus.time', bolusDose = 'bolus.dose',
                  gap = 'maxint', weight = 'weight'),
                pk.vars = 'date')

Build-PK-Oral Module

Description

This module builds PK data for orally administered medications.

Usage

run_Build_PK_Oral(
  x,
  idCol = "id",
  dtCol = "dt",
  doseCol = "dose",
  concCol = "conc",
  ldCol = NULL,
  first_interval_hours = 336,
  imputeClosest = NULL
)

Arguments

Details

See EHR Vignette for Build-PK-Oral.

Value

data.frame

Examples

## Data Generating Function
mkdat <- function() {
  npat <- 3
  visits <- floor(runif(npat, min=2, max=6))
  id <- rep(1:npat, visits)
  dt_samp <- as.Date(sort(sample(700, sum(visits))), origin = '2019-01-01')
  tm_samp <- as.POSIXct(paste(dt_samp, '10:00:00'), tz = 'UTC')
  dt <- tm_samp + rnorm(sum(visits), 0, 1*60*60)
  dose_morn <- sample(c(2.5,5,7.5,10), sum(visits), replace = TRUE)
  conc <- round(rnorm(sum(visits), 1.5*dose_morn, 1),1)
  ld <- dt - sample(10:16, sum(visits), replace = TRUE) * 3600
  ld[rnorm(sum(visits)) < .3] <- NA
  age <- rep(sample(40:75, npat), visits)
  gender <- rep(sample(0:1, npat, replace=TRUE), visits)
  weight <- rep(round(rnorm(npat, 180, 20)),visits)
  hgb <- rep(rnorm(npat, 10, 2), visits)
  data.frame(id, dt, dose_morn, conc, ld, age, gender, weight, hgb)
}

# Make raw data
set.seed(30)
dat <- mkdat()

#Process data without last-dose times
run_Build_PK_Oral(x = dat,
                  idCol = "id",
                  dtCol = "dt",
                  doseCol = "dose_morn",
                  concCol = "conc",
                  ldCol = NULL,
                  first_interval_hours = 336,
                  imputeClosest = NULL)

#Process data with last-dose times
run_Build_PK_Oral(x = dat, doseCol = "dose_morn", ldCol = "ld")

Run Demographic Data

Description

This module will load and modify demographic data.

Usage

run_Demo(demo.path, demo.columns = list(), toexclude, demo.mod.list)

Arguments

Details

See EHR Vignette for Structured Data.

Value

list with two components

Examples

set.seed(2525)
dateSeq <- seq(as.Date('2019/01/01'), as.Date('2020/01/01'), by="day")
demo <- data.frame(mod_id_visit = 1:10,
                   weight.lbs = rnorm(10,160,20),
                   age = rnorm(10, 50, 10),
                   enroll.date = sample(dateSeq, 10))
tmpfile <- paste0(tempfile(), '.rds')
saveRDS(demo, file = tmpfile)

# exclusion functions
exclude_wt <- function(x) x < 150
exclude_age <- function(x) x > 60
ind.risk <- function(wt, age) wt>170 & age>55
exclude_enroll <- function(x) x < as.Date('2019/04/01')

# make demographic data that:
# (1) excludes ids with weight.lbs < 150, age > 60, or enroll.date before 2019/04/01
# (2) creates new 'highrisk' variable for subjects with weight.lbs>170 and age>55
out <- run_Demo(demo.path = tmpfile, demo.columns = list(id = 'mod_id_visit'),
               toexclude = expression(
                 exclude_wt(weight.lbs)|exclude_age(age)|exclude_enroll(enroll.date)
               ),
               demo.mod.list = list(highrisk = expression(ind.risk(weight.lbs, age))))

out

Run Drug Level Data

Description

This module will load and modify drug-level data.

Usage

run_DrugLevel(
  conc.path,
  conc.columns = list(),
  conc.select,
  conc.rename,
  conc.mod.list = NULL,
  samp.path = NULL,
  samp.columns = list(),
  samp.mod.list = NULL,
  check.path = NULL,
  failmiss_fn = "MissingConcDate-",
  multsets_fn = "multipleSetsConc-",
  faildup_fn = "DuplicateConc-",
  drugname = NULL,
  LLOQ = NA,
  demo.list = NULL,
  demo.columns = list()
)

Arguments

Details

See EHR Vignette for Structured Data.

Value

drug-level data set

Examples

# concentrations
conc_data <- data.frame(mod_id = rep(1:3,each=4),
                       mod_visit = rep(c(2,1,1),each=4),
                       mod_id_visit = as.numeric(paste(rep(1:3,each=4),
                                                      rep(c(2,1,1),each=4), sep=".")),
                       samp = rep(1:4,times=3),
                       drug_calc_conc=15*exp(-1*rep(1:4,times=3))+rnorm(12,0,0.1))

# sample times
build_date <- function(x) format(seq(x, length.out=4, by="1 hour"), "%Y-%m-%d %H:%M")
dates <- unlist(lapply(rep(Sys.time(),3), build_date))

samp_data <- data.frame(mod_id = rep(1:3,each=4),
                       mod_visit = rep(c(2,1,1),each=4),
                       mod_id_visit = as.numeric(paste(rep(1:3,each=4),
                                                       rep(c(2,1,1),each=4), sep=".")),
                       samp = rep(1:4,times=3),
                       Sample.Collection.Date.and.Time = dates)

run_DrugLevel(
  conc.path = conc_data,
  conc.columns = list(
    id = 'mod_id', idvisit = 'mod_id_visit', samplinkid = 'mod_id_event', conc = 'conc.level'
  ),
  conc.select = c('mod_id','mod_id_visit','samp','drug_calc_conc'),
  conc.rename = c(drug_calc_conc= 'conc.level', samp='event'),
  conc.mod.list = list(mod_id_event = expression(paste(mod_id_visit, event, sep = "_"))),
  samp.path = samp_data,
  samp.columns = list(conclinkid = 'mod_id_event', datetime = 'Sample.Collection.Date.and.Time'),
  samp.mod.list = list(mod_id_event = expression(paste(mod_id_visit, samp, sep = "_"))),
  drugname = 'drugnm',
  LLOQ = 0.05
)

# minimal example with data in required format
conc_data <- conc_data[,c('mod_id','mod_id_visit','samp','drug_calc_conc')]
conc_data[,'mod_id_event'] <- paste(conc_data[,'mod_id_visit'], conc_data[,'samp'], sep = "_")
names(conc_data)[3:4] <- c('event','conc.level')
samp_data[,'mod_id_event'] <- paste(samp_data[,'mod_id_visit'], samp_data[,'samp'], sep = "_")
conc_samp_link <- match(conc_data[,'mod_id_event'], samp_data[,'mod_id_event'])
conc_date <- samp_data[conc_samp_link, 'Sample.Collection.Date.and.Time']
conc_data[,'date.time'] <- as.POSIXct(conc_date)
run_DrugLevel(conc_data, conc.columns = list(
  id = 'mod_id', idvisit = 'mod_id_visit', datetime = 'date.time', conc = 'conc.level'
))

Run Lab Data

Description

This module will load and modify laboratory data.

Usage

run_Labs(lab.path, lab.select, lab.mod.list)

Arguments

Details

See EHR Vignette for Structured Data.

Value

lab data set

Examples

lab_data <- data.frame(mod_id=rep(1:3,each=3),
                       date=rep(c("01/12/17","05/05/18","11/28/16"),each=3),
                       time=rep(c("1:30","2:30","3:30"),3),
                       creat=rnorm(9,0.5,0.05))
run_Labs(lab_data, lab.mod.list=list(log_creat=expression(log(creat))))

Run Str Data I

Description

This module will load and modify structured intravenous (IV) infusion and bolus medication data.

Usage

run_MedStrI(
  mar.path,
  mar.columns = list(),
  medGivenReq = FALSE,
  flow.path = NULL,
  flow.columns = list(),
  medchk.path = NULL,
  demo.list = NULL,
  demo.columns = list(),
  missing.wgt.path = NULL,
  wgt.columns = list(),
  check.path = NULL,
  failflow_fn = "FailFlow",
  failnounit_fn = "NoUnit",
  failunit_fn = "Unit",
  failnowgt_fn = "NoWgt",
  censor_date_fn = "CensorTime",
  infusion.unit = "mcg/kg/hr",
  bolus.unit = "mcg",
  bol.rate.thresh = Inf,
  rateunit = "mcg/hr",
  ratewgtunit = "mcg/kg/hr",
  weightunit = "kg",
  drugname = NULL
)

Arguments

Details

See EHR Vignette for Structured Data.

Value

structured data set

Examples

# flow data for 'Fakedrug1'
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[,'unit'] <- sub('.*[ ]', '', flow[,'Final.Rate'])
flow[,'rate'] <- as.numeric(sub('([0-9.]+).*', '\\1', flow[,'Final.Rate']))

# mar data for 4 fake drugs
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)

# medcheck file for drug of interest ('Fakedrug1')
medcheck <- data.frame(medname="Fakedrug1",freq=4672)

run_MedStrI(mar.path = mar,
            mar.columns = list(id = 'mod_id', datetime = c('Date','Time'),
                               dose = 'med:dosage', drug = 'med:mDrug', given = 'med:given'),
            flow.path = flow,
            flow.columns = list(id = 'mod_id', datetime = 'Perform.Date',
                                finalunits = 'Final.Units', unit = 'unit',
                                rate = 'rate', weight = 'Final.Weight'),
            medchk.path = medcheck,
            check.path = tempdir(),
            drugname = 'fakedrg1')

Run Structured E-Prescription Data

Description

This module will load and modify structured e-prescription data.

Usage

run_MedStrII(file, dat.columns = list())

Arguments

Details

See EHR Vignette for Structured Data.

Value

str data set

Examples

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"))

run_MedStrII(erx_data, list(id = 'GRID', dose = 'RX_DOSE', freq = 'FREQUENCY',
             date = 'ENTRY_DATE', str = 'STRENGTH_AMOUNT', desc = 'DESCRIPTION'))

Standardize Dose Entity

Description

This function standardizes the dose entity.

Usage

stdzDose(x)

Arguments

Details

Some dose strings may include multiple values and additional interpretation may be needed. For example ‘2-1’ likely indicates a dose of 2 followed by a dose of 1. Currently it would be converted to the average of 1.5.

Value

numeric vector

Examples

stdzDose(c('one tablet', '1/2 pill', '1-3 tabs'))

Standardize Dose Change Entity

Description

This function standardizes the dose change entity.

Usage

stdzDoseChange(x)

Arguments

Value

character vector

Examples

stdzDoseChange(c('decreasing','dropped','increased'))

Standardize Dose Schedule Entity

Description

This function standardizes the dose schedule entity.

Usage

stdzDoseSchedule(x)

Arguments

Value

character vector

Examples

stdzDoseSchedule(c('tapered','weaned','TAPER'))

Standardize Duration Entity

Description

This function standardizes the duration entity.

Usage

stdzDuration(x)

Arguments

Value

character vector

Examples

stdzDuration(c('1 month', 'three days', 'two-weeks'))

Standardize Frequency Entity

Description

This function standardizes the frequency entity.

Usage

stdzFreq(x)

Arguments

Value

character vector

Examples

stdzFreq(c('in the morning', 'four times a day', 'with meals'))

Standardize Route Entity

Description

This function standardizes the route entity.

Usage

stdzRoute(x)

Arguments

Value

character vector

Examples

stdzRoute(c('oral', 'po', 'subcut'))

Standardize Strength Entity

Description

This function standardizes the strength entity.

Usage

stdzStrength(str, freq)

Arguments

Details

Some strength strings may include multiple values and additional interpretation may be needed. For example ‘2-1’ likely indicates a strength of 2 followed by a strength of 1. Thus a single element may need to be standarized into two elements. This can only happen if the frequency entity is missing or in agreement (‘bid’ for example). See the ‘addl_data’ attribute of the returned vector.

Value

numeric vector

Examples

stdzStrength(c('1.5', '1/2', '1/1/1'))
stdzStrength(c('1.5', '1/2', '1/1/1'), c('am', 'daily', NA))
stdzStrength(c('1.5', '1/2', '1/1/1'), FALSE)

Example of Lab Time Data for Tacrolimus

Description

An example dataset used in processLastDose that contains lab time data. This dataset should have one row per patient ID-date pair, and contain the time a lab was performed as a datetime variable.

Usage

data(tac_lab, package = 'EHR')

Format

A data frame with 2 observations on the following variables.

pid

A character vector, patient ID associated with the lab value

date

A character vector, date associated with the lab value

labtime

A POSIXct vector, datetime at which the lab was performed formatted as YYYY-MM-DD HH:MM:SS

Examples

data(tac_lab)

Example of Metadata for Tacrolimus Data

Description

An example of the metadata needed for the processLastDose, makeDose, and collapseDose functions.

Usage

data(tac_metadata, package = 'EHR')

Format

A data frame with 5 observations on the following variables.

filename

A character vector, filename for the clinical note

pid

A character vector, patient ID associated with the filename

date

A character vector, date associated with the filename

note

A character vector, note ID associated with the filename

Examples

data(tac_metadata)

Example of Tacrolimus Output from 'parseMedExtractR'

Description

The output after running parseMedExtractR on 3 example clinical notes.

Usage

data(tac_mxr_parsed, package = 'EHR')

Format

A data frame with 7 observations on the following variables.

filename

A character vector, filename for the clinical note

drugname

A character vector, drug name extracted from the clinical note along with start and stop positions

strength

A character vector, strengths extracted from the clinical note along with start and stop positions

dose

A character vector, dose amounts extracted from the clinical note along with start and stop positions

route

A character vector, routes extracted from the clinical note along with start and stop positions

freq

A character vector, frequencies extracted from the clinical note along with start and stop positions

dosestr

A character vector, dose intakes extracted from the clinical note along with start and stop positions

dosechange

A character vector, dose change keywords extracted from the clinical note along with start and stop positions

lastdose

A character vector, last dose times extracted from the clinical note along with start and stop positions

Examples

data(tac_mxr_parsed)

Write Check File as CSV

Description

A check file contains IDs and data that need to be manually verified.

Usage

writeCheckData(dat, filename, msg, decor = TRUE, ...)

Arguments

Make Zero One Contingency Tables

Description

Make contingency tables for many binary outcomes and a binary covariate

Usage

zeroOneTable(EXPOSURE, phenotype)

Arguments

Details

Generates frequency and contingency tables for many binary outcomes (e.g., large number of phenotypes) and a binary covariate (e.g., drug exposure, genotypes) more efficiently.

Value

Author(s)

Leena Choi leena.choi@vanderbilt.edu and Cole Beck cole.beck@vumc.org

Examples

## full example data
data(dataPheWAS)
demo.covariates <- c('id','exposure','age','race','gender')
phenotypeList <- setdiff(colnames(dd), demo.covariates)
tablePhenotype <- matrix(NA, ncol=4, nrow=length(phenotypeList), 
dimnames=list(phenotypeList, c("n.nocase.nonexp", "n.case.nonexp", 
"n.nocase.exp", "n.case.exp")))
for(i in seq_along(phenotypeList)) {
    tablePhenotype[i, ] <- zeroOneTable(dd[, 'exposure'], dd[, phenotypeList[i]])
}