, Cole Beck [aut]
, Hannah Weeks [aut]
| Title: | Extraction of Medication Information from Clinical Text |
|---|---|
| Description: | Function and support for medication and dosing information extraction from free-text clinical notes. Medication entities for the basic medExtractR implementation that can be extracted include drug name, strength, dose amount, dose, frequency, intake time, dose change, and time of last dose. The basic medExtractR is outlined in Weeks, Beck, McNeer, Williams, Bejan, Denny, Choi (2020) <doi: 10.1093/jamia/ocz207>. The extended medExtractR_tapering implementation is intended to extract dosing information for more tapering schedules, which are far more complex. The tapering extension allows for the extraction of additional entities including dispense amount, refills, dose schedule, time keyword, transition, and preposition. |
| Authors: | Leena Choi [aut, cre]
, Cole Beck [aut]
, Hannah Weeks [aut]
|
| Maintainer: | Leena Choi <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.4.1 |
| Built: | 2024-11-21 06:43:59 UTC |
| Source: | CRAN |
Provides a function medExtractR for extracting
dose attributes for medications within a given electronic health record (EHR) note.
Hannah Weeks [email protected],
Cole Beck [email protected],
Leena Choi [email protected]
Maintainer: Leena Choi [email protected]
note1 <- "Progrf Oral Capsule 1 mg 3 capsules by mouth twice a day - last dose at 10pm" note2 <- "Currently on lamotrigine 150-200, but will increase to lamotrigine 200mg bid" medExtractR(note1, c("prograf", "tacrolimus"), 60, "mg", 2, lastdose=TRUE) medExtractR(note2, c("lamotrigine", "ltg"), 130, "mg", 1, strength_sep = "-")note1 <- "Progrf Oral Capsule 1 mg 3 capsules by mouth twice a day - last dose at 10pm" note2 <- "Currently on lamotrigine 150-200, but will increase to lamotrigine 200mg bid" medExtractR(note1, c("prograf", "tacrolimus"), 60, "mg", 2, lastdose=TRUE) medExtractR(note2, c("lamotrigine", "ltg"), 130, "mg", 1, strength_sep = "-")
drug_list
A dictionary with additional expressions that can be used to supplement the drug_list
argument of medExtractR and medExtractR_tapering.
addl_expraddl_expr
A data frame with the following variables:
A character vector, additional optional expressions for the drug_list argument.
A character vector, what category the expression belongs to (e.g., symptom, lab name, medication abbreviation, or drug class).
data(addl_expr)data(addl_expr)
A dictionary of words indicating a dose change, meaning that the associated drug regimen may not be current. This includes phrases such as increase, reduce, or switch. In the following example of clinical text, the word ‘increase’ represents a dose change keyword: “Increase prograf to 5mg bid.”
dosechange_valsdosechange_vals
A data frame with dose change expressions (exact and/or regular expressions).
A character vector, expressions to consider as dose change.
data(dosechange_vals)data(dosechange_vals)
A dictionary with words for indicating a tapering dosing schedule. These can explicitly refer to such a schedule with phrases like "tapering" or "wean". It also includes words indicating an alternating dose schedule (e.g., "alternate", "alt.", "even days", or "odd days") as well as stopping keywords indicating the patient is going completely off the medication (e.g., "done", "gone", "stop", "discontinue").
doseschedule_valsdoseschedule_vals
A data frame with dose schedule expressions (exact and/or regular expressions).
A character vector, expressions to consider as dose schedule.
data(doseschedule_vals)data(doseschedule_vals)
A dictionary with phrases indicating how long the patient should take a particular dose of the drug. Examples of duration expressions include "2 weeks", "14 days", "another 3 days", "through mid-April", or a specific date. The form of each duration is given as a regular expression.
duration_valsduration_vals
A data frame with duration expressions (exact and/or regular expressions).
A character vector, expressions to consider as duration.
data(duration_vals)data(duration_vals)
This function searches a phrase for medication dosing entities of interest. It
is called within medExtractR and generally not intended for use outside
that function. The phrase argument containing text to search corresponds to an
individual mention of the drug of interest.
extract_entities( phrase, p_start, p_stop, unit, frequency_fun = NULL, intaketime_fun = NULL, duration_fun = NULL, route_fun = NULL, strength_sep = NULL, ... )extract_entities( phrase, p_start, p_stop, unit, frequency_fun = NULL, intaketime_fun = NULL, duration_fun = NULL, route_fun = NULL, strength_sep = NULL, ... )
phrase |
Text to search. |
p_start |
Start position of phrase within original text. |
p_stop |
End position of phrase within original text. |
unit |
Unit of measurement for medication strength, e.g. ‘mg’. |
frequency_fun |
Function used to extract frequency. |
intaketime_fun |
Function used to extract intake time. |
duration_fun |
Function used to extract duration. |
route_fun |
Function used to extract route. |
strength_sep |
Delimiter for contiguous medication strengths. |
... |
Parameter settings used in extracting frequency and intake time,
including additional arguments to the |
Various medication dosing entities are extracted within this function including the following:
strength: The amount of drug in a given dosage form (i.e., tablet, capsule).
dose amount: The number of tablets, capsules, etc. taken at a given intake time.
dose strength: The total amount of drug given intake. This quantity would be
equivalent to strength x dose amount, and appears similar to strength when
dose amount is absent.
frequency: The number of times per day a dose is taken, e.g.,
“once daily” or ‘2x/day’.
intaketime: The time period of the day during which a dose is taken,
e.g., ‘morning’, ‘lunch’, ‘in the pm’.
duration: How long a patient is on a drug regimen, e.g., ‘2 weeks’,
‘mid-April’, ‘another 3 days’.
route: The administration route of the drug, e.g., ‘by mouth’,
‘IV’, ‘topical’.
Note that extraction of the entities drug name, dose change, and time of last dose are not
handled by the extract_entities function. Those entities are extracted separately
and appended to the extract_entities output within the main medExtractR function.
Strength, dose amount, and dose strength are primarily numeric quantities, and are identified
using a combination of regular expressions and rule-based approaches. Frequency, intake time,
route, and duration, on the other hand, use dictionaries for identification.
By default and when an argument <entity>_fun is NULL, the
extract_generic function will be used to extract that entity. This function
can also inherit user-defined entity dictionaries, supplied as arguments <entity>_dict
to medExtractR or medExtractR_tapering (see documentation files for main function(s) for details).
The stength_sep argument is NULL by default, but can be used to
identify shorthand for morning and evening doses. For example, consider the
phrase “Lamotrigine 300-200” (meaning 300 mg in the morning and 200 mg
in the evening). The argument strength_sep = '-' identifies
the full expression 300-200 as dose strength in this phrase.
data.frame with entities information. At least one row per entity is returned,
using NA when no expression was found for a given entity.
The “entity” column of the output contains the formatted label for that entity, according to
the following mapping.
strength: “Strength”
dose amount: “DoseAmt”
dose strength: “DoseStrength”
frequency: “Frequency”
intake time: “IntakeTime”
duration: “Duration”
route: “Route”
Sample output for the phrase “Lamotrigine 200mg bid” would look like:
| entity | expr |
| IntakeTime | <NA> |
| Strength | <NA> |
| DoseAmt | <NA> |
| Route | <NA> |
| Duration | <NA> |
| Frequency | bid;19:22 |
| DoseStrength | 200mg;13:18 |
note <- "Lamotrigine 25 mg tablet - 3 tablets oral twice daily" extract_entities(note, 1, nchar(note), "mg") # A user-defined dictionary can be used instead of the default my_dictionary <- data.frame(c("daily", "twice daily")) extract_entities(note, 1, 53, "mg", frequency_dict = my_dictionary)note <- "Lamotrigine 25 mg tablet - 3 tablets oral twice daily" extract_entities(note, 1, nchar(note), "mg") # A user-defined dictionary can be used instead of the default my_dictionary <- data.frame(c("daily", "twice daily")) extract_entities(note, 1, 53, "mg", frequency_dict = my_dictionary)
extract_entities for Tapering applicationThis function searches a phrase for medication dosing entities of interest. It
is called within medExtractR_tapering and generally not intended for use outside
that function.
extract_entities_tapering( phrase, p_start, d_stop, unit, frequency_fun = NULL, intaketime_fun = NULL, duration_fun = NULL, route_fun = NULL, doseschedule_fun = NULL, preposition_fun = NULL, timekeyword_fun = NULL, transition_fun = NULL, dosechange_fun = NULL, strength_sep = NULL, ... )extract_entities_tapering( phrase, p_start, d_stop, unit, frequency_fun = NULL, intaketime_fun = NULL, duration_fun = NULL, route_fun = NULL, doseschedule_fun = NULL, preposition_fun = NULL, timekeyword_fun = NULL, transition_fun = NULL, dosechange_fun = NULL, strength_sep = NULL, ... )
phrase |
Text to search. |
p_start |
Start position of phrase within original text. |
d_stop |
End position of drug name within original text. |
unit |
Unit of measurement for medication strength, e.g., ‘mg’. |
frequency_fun |
Function used to extract frequency. |
intaketime_fun |
Function used to extract intake time. |
duration_fun |
Function used to extract duration. |
route_fun |
Function used to extract route. |
doseschedule_fun |
Function used to extract dose schedule. |
preposition_fun |
Function used to extract preposition. |
timekeyword_fun |
Function used to extract time keyword. |
transition_fun |
Function used to extract transition. |
dosechange_fun |
Function used to extract dose change. |
strength_sep |
Delimiter for contiguous medication strengths. |
... |
Parameter settings used in extracting frequency and intake time,
including additional arguments to |
Various medication dosing entities are extracted within this function including the following:
strength: The amount of drug in a given dosage form (i.e., tablet, capsule).
dose amount: The number of tablets, capsules, etc. taken at a given intake time.
dose strength: The total amount of drug given intake. This quantity would be
equivalent to strength x dose amount, and appears similar to strength when
dose amount is absent.
frequency: The number of times per day a dose is taken, e.g.,
“once daily” or ‘2x/day’.
intaketime: The time period of the day during which a dose is taken,
e.g., ‘morning’, ‘lunch’, ‘in the pm’.
duration: How long a patient is on a drug regimen, e.g., ‘2 weeks’,
‘mid-April’, ‘another 3 days’.
route: The administration route of the drug, e.g., ‘by mouth’,
‘IV’, ‘topical’.
dose change: Whether the dosage of the drug was changed, e.g.,
‘increase’, ‘adjust’, ‘reduce’.
dose schedule: Keywords which represent special dosing regimens, such as tapering
schedules, alternating doses, or stopping keywords, e.g., ‘weaning’,
‘even days’ or ‘odd_days’, ‘discontinue’.
time keyword: Whether the dosing regimen is a past dose, current dose,
or future dose, e.g., ‘currently’, ‘remain’, ‘yesterday’.
transition: Words or symbols that link consecutive doses of a tapering
regimen, e.g., ‘then’, ‘followed by’, or a comma ‘,’.
preposition: Prepositions that occur immediately next to another
identified entity, e.g., ‘to’, ‘until’, ‘for’.
dispense amount: The number of pills prescribed to the patient.
refill: The number of refills allowed for the patient's prescription.
Similar to the basic implementation, drug name and and time of last dose are not
handled by the extract_entities_tapering function. Those entities are extracted separately
and appended to the extract_entities_tapering output within the main medExtractR_tapering
function. In the tapering extension, however, dose change is treated the same as other dictionary-based
entities and extracted within extract_entities_tapering. Strength, dose amount, dose strength, dispense amount,
and refill are primarily numeric quantities, and are identified using a combination of
regular expressions and rule-based approaches. All other entities use dictionaries for
identification. For more information about the default dictionary for a specific entity,
view the documentation file for the object <entity>_vals.
By default and when an argument <entity>_fun is NULL, the
extract_generic function will be used to extract that entity. This function
can also inherit user-defined entity dictionaries for each entity, supplied as arguments <entity>_dict
to medExtractR or medExtractR_tapering (see documentation files for main function(s) for details).
Note that extract_entities_tapering has the argument d_stop. This differs
from extract_entities, which uses the end position of the full search window. This
is a consequence of medExtractR using a fixed search window length and medExtractR_tapering
dynamically constructing a search window.
data.frame with entities information. At least one row per entity is returned,
using NA when no expression was found for a given entity.
The “entity” column of the output contains the formatted label for that entity, according to
the following mapping.
strength: “Strength”
dose amount: “DoseAmt”
dose strength: “DoseStrength”
frequency: “Frequency”
intake time: “IntakeTime”
duration: “Duration”
route: “Route”
dose change: “DoseChange”
dose schedule: “DoseScheule”
time keyword: “TimeKeyword”
transition: “Transition”
preposition: “Preposition”
dispense amount: “DispenseAmt”
refill: “Refill”
Sample output for the phrase “Lamotrigine 200mg bid for 14 days” would look like:
| entity | expr |
| IntakeTime | <NA> |
| Strength | <NA> |
| DoseAmt | <NA> |
| DoseChange | <NA> |
| DoseSchedule | <NA> |
| TimeKeyword | <NA> |
| Transition | <NA> |
| Preposition | <NA> |
| DispenseAmt | <NA> |
| Refill | <NA> |
| Frequency | bid;19:22 |
| DoseStrength | 200mg;13:18 |
| Preposition | for;23:26 |
| Duration | 14 days;27:34 |
note <- "prednisone 20mg daily tapering to 5mg daily over 2 weeks" extract_entities_tapering(note, 1, 11, "mg") # A user-defined dictionary can be used instead of the default my_dictionary <- data.frame(c("daily", "twice daily")) extract_entities(note, 1, 11, "mg", frequency_dict = my_dictionary)note <- "prednisone 20mg daily tapering to 5mg daily over 2 weeks" extract_entities_tapering(note, 1, 11, "mg") # A user-defined dictionary can be used instead of the default my_dictionary <- data.frame(c("daily", "twice daily")) extract_entities(note, 1, 11, "mg", frequency_dict = my_dictionary)
This function searches a phrase for the position and length of expressions specified in a dictionary. This is called within other main functions of the package and generally not intended for use on its own.
extract_generic(phrase, dict)extract_generic(phrase, dict)
phrase |
Text to search. |
dict |
data.frame, the first column should contain expressions to find. These can be regular expressions or exact phrases. |
extract_generic is used to extract entities that are
identified with an associated dictionary of phrases or regular expressions,
such as dose change, frequency, intake time, route, or duration in
medExtractR and medExtractR_tapering, as well as
dose schedule, time keyword, transition, and preposition in medExtractR_tapering. This function
is called within extract_entities.
A numeric matrix with position and expression length.
data(frequency_vals) extract_generic("take two every day", dict = frequency_vals) extract_generic("take two every morning", dict = data.frame(c("morning", "every morning")))data(frequency_vals) extract_generic("take two every day", dict = frequency_vals) extract_generic("take two every morning", dict = data.frame(c("morning", "every morning")))
This function searches a phrase for the expression and postion
of the time at which the last dose of a drug was taken. It
is called within medExtractR and generally not intended for use outside
that function.
extract_lastdose(phrase, p_start, d_start, d_stop, time_exp = "default")extract_lastdose(phrase, p_start, d_start, d_stop, time_exp = "default")
phrase |
Text to search. |
p_start |
Start position of phrase in the overall text (e.g., the full clinical note). |
d_start |
Start position of drug name in larger text. |
d_stop |
End position of drug name in larger text. |
time_exp |
Vector of regular expressions to identify time expressions. |
This function identifies the time at which the last dose of a drug of interest was taken.
The arguments p_start, d_start, and d_stop represent global start or stop
positions for the phrase or drug. These arguments are used to determine the position of any found
last dose time expressions relative to the overall clinical note, not just within phrase.
The time_exp argument contains regular expressions for numeric or text representations of
last dose time. See time_regex for more information about the default regular
expressions used in medExtractR.
data.frame with last dose time entity information. This output format is consistent with
the output of extract_entities, and the formatted label for the time of last dose entity
is "LastDose."
Sample output for the phrase “Last prograf at 5pm” would look like:
| entity | expr |
| LastDose | 5pm;17:20 |
# Suppose this phrase begins at character 120 in the overall clinical note extract_lastdose("took aspirin last night at 8pm", p_start = 120, d_start = 125, d_stop = 131)# Suppose this phrase begins at character 120 in the overall clinical note extract_lastdose("took aspirin last night at 8pm", p_start = 120, d_start = 125, d_stop = 131)
A dictionary mapping frequency expressions to numeric values representing the corresponding number of doses per day. Example expressions include "q12 hours", "bid", "daily", and "three times a day". The form of each frequency is given as a regular expression.
frequency_valsfrequency_vals
A data frame with frequency expressions (exact and/or regular expressions).
A character vector, expressions to consider as frequency.
A numeric vector, numeric value of frequency represented as number of doses taken per day. For example, “bid” and “twice a day” would both have a numeric value of 2.
data(frequency_vals)data(frequency_vals)
A dictionary with intake time expressions representing the approximate time of day when a dose should be taken. Example expressions include "in the morning", "with lunch", "at bedtime", and "qpm". The form of each intake time is given as a regular expression.
intaketime_valsintaketime_vals
A data frame with intake time expressions (exact and/or regular expressions).
A character vector, expressions to consider as intake time.
data(intaketime_vals)data(intaketime_vals)
This function identifies medication entities of interest and returns found expressions with start and stop positions.
medExtractR( note, drug_names, window_length, unit, max_dist = 0, drug_list = "rxnorm", lastdose = FALSE, lastdose_window_ext = 1.5, strength_sep = NULL, flag_window = 30, dosechange_dict = "default", ... )medExtractR( note, drug_names, window_length, unit, max_dist = 0, drug_list = "rxnorm", lastdose = FALSE, lastdose_window_ext = 1.5, strength_sep = NULL, flag_window = 30, dosechange_dict = "default", ... )
note |
Text to search. |
drug_names |
Vector of drug names of interest to locate. |
window_length |
Length (in number of characters) of window after drug in which to look. |
unit |
Strength unit to look for (e.g., ‘mg’). |
max_dist |
Numeric - edit distance to use when searching for |
drug_list |
Vector of known drugs that may end search window. By default calls
|
lastdose |
Logical - whether or not last dose time entity should be extracted. |
lastdose_window_ext |
Numeric - multiplicative factor by which
|
strength_sep |
Delimiter for contiguous medication strengths (e.g., ‘-’ for “LTG 200-300”). |
flag_window |
How far around drug (in number of characters) to look for dose change keyword - default fixed to 30. See ‘Details’ section below for further explanation. |
dosechange_dict |
List of keywords used to determine if a dose change entity is present. |
... |
Parameter settings used in extracting frequency, intake time, route, and duration. Potentially useful
parameters include |
This function uses a combination of regular expressions, rule-based
approaches, and dictionaries to identify various drug entities of interest.
Specific medications to be found are specified with drug_names, which
is not case-sensitive or space-sensitive (e.g., ‘lamotrigine XR’ is treated
the same as ‘lamotrigineXR’). Entities to be extracted include drug name, strength,
dose amount, dose, frequency, intake time, route, duration, and time of last dose. See
extract_entities and extract_lastdose for more details.
When searching for medication names of interest, fuzzy matching may be used.
The max_dist argument determines the maximum edit distance allowed for
such matches. If using fuzzy matching, any drug name with less than 5
characters will only allow an edit distance of 1, regardless of the value of
max_dist.
The purpose of the drug_list argument is to reduce false positives by removing information that is
likely to be related to a competing drug, not our drug of interest, By default, this is “rxnorm” which
calls data(rxnorm_druglist). A custom drug list in the form of a character string can be supplied instead,
or can be appended to rxnorm_druglist by specifying drug_list = c("rxnorm", custom_drug_list).
medExtractR then uses this list to truncate the search window at the first appearance of an unrelated drug name.
This uses publicly available data courtesy of the U.S. National Library of Medicine (NLM), National
Institutes of Health, Department of Health and Human Services; NLM is not responsible for the product and
does not endorse or recommend this or any other product. See rxnorm_druglist documentation for details.
Most medication entities are searched for in a window after the drug. The
dose change entity, or presence of a keyword to indicate a non-current drug
regimen, may occur before the drug name. The flag_window argument
adjusts the width of the pre-drug window. Both flag_window and dosechange_dict
are not default arguments to the extended function medExtractR_tapering since that
extension uses a more flexible search window and extraction procedure. In the tapering extension,
entity extraction is more flexible, and any entity can be extracted either before
or after the drug mention. Thus functionality for dose change identification is identical to all
other dictionary-based entities.
The stength_sep argument is NULL by default, but can be used to
identify shorthand for morning and evening doses. For example, consider the
phrase ‘Lamotrigine 300-200’ (meaning 300 mg in the morning and 200 mg
in the evening). The argument strength_sep = '-' identifies
the full expression 300-200 as dose strength in this phrase.
data.frame with entity information. Only extractions from found entities are returned. If no dosing
information for the drug of interest is found, the following output will be returned:
| entity | expr | pos |
| NA | NA | NA |
The “entity” column of the output contains the formatted label for that entity, according to
the following mapping.
drug name: “DrugName”
strength: “Strength”
dose amount: “DoseAmt”
dose strength: “DoseStrength”
frequency: “Frequency”
intake time: “IntakeTime”
duration: “Duration”
route: “Route”
dose change: “DoseChange”
time of last dose: “LastDose”
Sample output:
| entity | expr | pos |
| DoseChange | decrease | 66:74 |
| DrugName | Prograf | 78:85 |
| Strength | 2 mg | 86:90 |
| DoseAmt | 1 | 91:92 |
| Route | by mouth | 100:108 |
| Frequency | bid | 109:112 |
| LastDose | 2100 | 129:133 |
Nelson SJ, Zeng K, Kilbourne J, Powell T, Moore R. Normalized names for clinical drugs: RxNorm at 6 years. J Am Med Inform Assoc. 2011 Jul-Aug;18(4)441-8. doi: 10.1136/amiajnl-2011-000116. Epub 2011 Apr 21. PubMed PMID: 21515544; PubMed Central PMCID: PMC3128404.
note1 <- "Progrf Oral Capsule 1 mg 3 capsules by mouth twice a day - last dose at 10pm" medExtractR(note1, c("prograf", "tacrolimus"), 60, "mg", 2, lastdose=TRUE) note2 <- "Currently on lamotrigine 150-200, but will increase to lamotrigine 200mg bid" medExtractR(note2, c("lamotrigine", "ltg"), 130, "mg", 1, strength_sep = "-")note1 <- "Progrf Oral Capsule 1 mg 3 capsules by mouth twice a day - last dose at 10pm" medExtractR(note1, c("prograf", "tacrolimus"), 60, "mg", 2, lastdose=TRUE) note2 <- "Currently on lamotrigine 150-200, but will increase to lamotrigine 200mg bid" medExtractR(note2, c("lamotrigine", "ltg"), 130, "mg", 1, strength_sep = "-")
medExtractR for Tapering applicationsThis function identifies medication entities of interest and returns found expressions with start and stop positions.
medExtractR_tapering( note, drug_names, unit, max_dist = 0, drug_list = "rxnorm", lastdose = FALSE, strength_sep = NULL, ... )medExtractR_tapering( note, drug_names, unit, max_dist = 0, drug_list = "rxnorm", lastdose = FALSE, strength_sep = NULL, ... )
note |
Text to search. |
drug_names |
Vector of drug names of interest to locate. |
unit |
Strength unit to look for (e.g., ‘mg’). |
max_dist |
Numeric - edit distance to use when searching for |
drug_list |
Vector of known drugs that may end search window. By default calls
|
lastdose |
Logical - whether or not last dose time entity should be extracted. See ‘Details’ section below for more information. |
strength_sep |
Delimiter for contiguous medication strengths (e.g., ‘-’ for “LTG 200-300”). |
... |
Parameter settings used in dictionary-based entities. For each dictionary-based
entity, the user can supply the optional arguments |
This function uses a combination of regular expressions, rule-based
approaches, and dictionaries to identify various drug entities of interest, with a
particular focus on drugs administered with a tapering schedule.
Specific medications to be found are specified with drug_names, which
is not case-sensitive or space-sensitive (e.g., ‘lamotrigine XR’ is treated
the same as ‘lamotrigineXR’). Entities to be extracted include drug name, strength,
dose amount, dose strength, frequency, intake time, route, duration, dose schedule,
time keyword, preposition, transition, dispense amount, refill, and time of last dose.
While it is still an optional entity in medExtractR_tapering, if lastdose=TRUE
then medExtractR_tapering will search for time of last dose in the same search window used for all
other entities. As a result, there is no need for the lastdose_window_ext argument. See
extract_entities_tapering and extract_lastdose for more details.
When searching for medication names of interest, fuzzy matching may be used.
The max_dist argument determines the maximum edit distance allowed for
such matches. If using fuzzy matching, any drug name with less than 7 characters
will force an exact match, regardless of the value of max_dist. The default value of 7 was
selected based on a set of training notes for the drug prednisone, and differs slightly from the default
values of 5 for medExtractR. The tapering extension does not use the window_length argument
to define the search window, since tapering schedules can be much longer than a static regimens.
Instead, medExtractR_tapering dynamically generates the search window based on competing drug names or
phrases, and the distance between consecutive entities. The stength_sep argument is NULL by
default, and operates in the same manner as it does in medExtractR.
By default, the drug_list argument is “rxnorm” which calls data(rxnorm_druglist).
A custom drug list in the form of a character string can be supplied instead, or can be appended
to rxnorm_druglist by specifying drug_list = c("rxnorm", custom_drug_list). This uses
publicly available data courtesy of the U.S. National Library of Medicine (NLM), National
Institutes of Health, Department of Health and Human Services; NLM is not responsible for the product and
does not endorse or recommend this or any other product. See rxnorm_druglist documentation for details.
data.frame with entity information. If no dosing
information for the drug of interest is found, the following output will be returned:
| entity | expr | pos |
| NA | NA | NA |
The “entity” column of the output contains the formatted label for that entity, according to
the following mapping.
drug name: “DrugName”
strength: “Strength”
dose amount: “DoseAmt”
dose strength: “DoseStrength”
frequency: “Frequency”
intake time: “IntakeTime”
duration: “Duration”
route: “Route”
dose change: “DoseChange”
dose schedule: “DoseScheule”
time keyword: “TimeKeyword”
transition: “Transition”
preposition: “Preposition”
dispense amount: “DispenseAmt”
refill: “Refill”
time of last dose: “LastDose”
Sample output:
| entity | expr | pos |
| DoseChange | decrease | 66:74 |
| DrugName | Prograf | 78:85 |
| Strength | 2 mg | 86:90 |
| DoseAmt | 1 | 91:92 |
| Frequency | bid | 101:104 |
| LastDose | 2100 | 121:125 |
Nelson SJ, Zeng K, Kilbourne J, Powell T, Moore R. Normalized names for clinical drugs: RxNorm at 6 years. J Am Med Inform Assoc. 2011 Jul-Aug;18(4)441-8. doi: 10.1136/amiajnl-2011-000116. Epub 2011 Apr 21. PubMed PMID: 21515544; PubMed Central PMCID: PMC3128404.
A dictionary with preposition expressions. Such expressions often represent a relationship with an adjacent entity. Since most expressions in this dictionary are very short, we require word boundaries (any character other than a letter or number) to appear on either side of the expression. Example expressions include "for", "to", "until", and "in".
preposition_valspreposition_vals
A data frame with preposition expressions (exact and/or regular expressions).
A character vector, expressions to consider as preposition.
data(preposition_vals)data(preposition_vals)
A dictionary mapping route expressions to standardized forms, specifying the way in which a medication is administered. Example expressions include "oral", "topical", "IV", and "intravenous".
route_valsroute_vals
A data frame with route expressions (exact and/or regular expressions).
A character vector, expressions to consider as route.
A standardized version of the raw expression. For example, "orally" and "by mouth" both have the standardized form "orally".
data(route_vals)data(route_vals)
A dictionary that contains a vector of medication names, primarily derived from RxNorm.
rxnorm_druglistrxnorm_druglist
A vector with character strings for competing drug names.
RxNorm is provided by the U.S. National Library of Medicine. This dictionary uses the February 1, 2021 RxNorm files directly downloaded from https://www.nlm.nih.gov/research/umls/rxnorm/docs/rxnormfiles.html.
This list contains ingredient and brand names, cleaned to remove expressions that likely are ambiguous (e.g., ‘today’ or ‘date’). This product uses publicly available data courtesy of the U.S. National Library of Medicine (NLM), National Institutes of Health, Department of Health and Human Services; NLM is not responsible for the product and does not endorse or recommend this or any other product.
Nelson SJ, Zeng K, Kilbourne J, Powell T, Moore R. Normalized names for clinical drugs: RxNorm at 6 years. J Am Med Inform Assoc. 2011 Jul-Aug;18(4)441-8. doi: 10.1136/amiajnl-2011-000116. Epub 2011 Apr 21. PubMed PMID: 21515544; PubMed Central PMCID: PMC3128404.
data(rxnorm_druglist)data(rxnorm_druglist)
This function counts occurrences of text within one or more phrases.
string_counts(strings, search_data, ignore.case = TRUE)string_counts(strings, search_data, ignore.case = TRUE)
strings |
character vector; value(s) to find |
search_data |
character vector; phrase(s) where values may exist |
ignore.case |
logical; indicates if spelling case matters, defaulting to ‘TRUE’ |
list with two elements; ‘cntByTotal’ contains total occurrences and ‘cntByData’ contains occurrences for each element in ‘search_data’
note1 <- "I am the very model of a modern major general I've information vegetable, animal, and mineral I know the kings of England, and I quote the fights historical From marathon to Waterloo in order categorical; I'm very well acquainted, too, with matters mathematical, I understand equations both the simple and quadratical About binomial theorem I'm teeming with a lot o' news, With many cheerful facts about the square of the hypotenuse" note2 <- "The quick brown fox jumps over the lazy dog" string_counts(c('I','the','couth'), c(note1, note2))note1 <- "I am the very model of a modern major general I've information vegetable, animal, and mineral I know the kings of England, and I quote the fights historical From marathon to Waterloo in order categorical; I'm very well acquainted, too, with matters mathematical, I understand equations both the simple and quadratical About binomial theorem I'm teeming with a lot o' news, With many cheerful facts about the square of the hypotenuse" note2 <- "The quick brown fox jumps over the lazy dog" string_counts(c('I','the','couth'), c(note1, note2))
This function searches for text within one or more phrases. Text to look for will be grouped into values that are found and not found.
string_occurs(dict_list, haystack, ignore.case = TRUE, nClust = 2)string_occurs(dict_list, haystack, ignore.case = TRUE, nClust = 2)
dict_list |
character vector; value(s) to find |
haystack |
character vector; phrase(s) where values may exist |
ignore.case |
logical; indicates if spelling case matters, defaulting to ‘TRUE’ |
nClust |
Number of CPU cores to use, if available. This requires the ‘parallel’ package. |
list with two elements, ‘TRUE’ and ‘FALSE’, representing values that are found or not found within the phrase to search.
note1 <- "I am the very model of a modern major general I've information vegetable, animal, and mineral I know the kings of England, and I quote the fights historical From marathon to Waterloo in order categorical; I'm very well acquainted, too, with matters mathematical, I understand equations both the simple and quadratical About binomial theorem I'm teeming with a lot o' news, With many cheerful facts about the square of the hypotenuse" note2 <- "The quick brown fox jumps over the lazy dog" string_occurs(c('kings','quick','couth','brown'), c(note1, note2))note1 <- "I am the very model of a modern major general I've information vegetable, animal, and mineral I know the kings of England, and I quote the fights historical From marathon to Waterloo in order categorical; I'm very well acquainted, too, with matters mathematical, I understand equations both the simple and quadratical About binomial theorem I'm teeming with a lot o' news, With many cheerful facts about the square of the hypotenuse" note2 <- "The quick brown fox jumps over the lazy dog" string_occurs(c('kings','quick','couth','brown'), c(note1, note2))
This function searches for text within one or more phrases, and looks for partial matches. An exact match of the text should be found in order for a suggestion to made.
string_suggestions(strings, search_data, max_dist = 2, ignore.case = TRUE)string_suggestions(strings, search_data, max_dist = 2, ignore.case = TRUE)
strings |
character vector; value(s) to find |
search_data |
character vector; phrase(s) where values may exist |
max_dist |
numeric; edit distance to use for partial matches. The default value is 2. |
ignore.case |
logical; indicates if spelling case matters, defaulting to ‘TRUE’ |
data.frame with two columns, ‘suggestion’ and ‘match’
string_suggestions('penicillin', 'penicillan, penicillin, or penicilin?')string_suggestions('penicillin', 'penicillan, penicillin, or penicilin?')
A vector of regular expressions to identify different forms of time
expressions for last dose time. These are the default values used in link{extract_lastdose}.
time_regextime_regex
A vector with 5 regular expressions for the following categories.
Time is indicated by the presence of ‘am’ or ‘pm’ following a numeric expression.
Time is given in military time, for unambiguous times of 13:00-23:59.
Am/pm indication is implicit through a qualifying term like ‘last night’ or ‘this morning’. The qualifier occurs after the time, e.g., ‘10 last night.’
Am/pm indication is implicit through a qualifying term like ‘last night’ or ‘this morning’. The qualifier occurs before the time, e.g., ‘last night at 10.’
Time (in hours) between the last dose and most recent lab value
Certain expressions which might be considered ambiguous are excluded from the regular expressions presented here. For instance, expressions such as ‘600’ could refer to either 6am or 6pm.
data(time_regex)data(time_regex)
A dictionary with time keyword expressions representing whether the dosing regimen is past, current, or future. Example expressions include "currently", "remain","not taking", "yesterday", and "past".
timekeyword_valstimekeyword_vals
A data frame with time keyword expressions (exact and/or regular expressions).
A character vector, expressions to consider as time keyword.
data(timekeyword_vals)data(timekeyword_vals)
A dictionary with transition symbols and expressions representing a break between consecutive doses within a tapering regimen. This dictionary includes the expressions "then" and "followed by", as well as the punctuation ",(?!\\s?then)" or ";(?!\\s?then)" (i.e., a comma or semicolon not followed by the word "then").
transition_valstransition_vals
A data frame with transition expressions (exact and/or regular expressions).
A character vector, expressions to consider as transitions.
data(transition_vals)data(transition_vals)