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Navigating the shift of clinical laboratory data from primary
everyday clinical use to secondary research purposes presents a
significant challenge. Given the substantial time and expertise required
to preprocess and clean this data and the lack of all-in-one tools
tailored for this need, we developed our algorithm
lab2clean as an open-source R-package.
lab2clean package is set to automate and standardize the
intricate process of cleaning clinical laboratory results. With a keen
focus on improving the data quality of laboratory result values, while
assuming compliance with established standards like LOINC and UCUM for
test identifiers and units, our goal is to equip researchers with a
straightforward, plug-and-play tool, making it smoother for them to
unlock the true potential of clinical laboratory data in clinical
research and clinical machine learning (ML) model development. Version
1.0 of the algorithm is described in detail in Zayed et al. 2024 [https://doi.org/10.1186/s12911-024-02652-7]
The lab2clean package contains two key functions:
clean_lab_result() and validate_lab_result().
The clean_lab_result() function cleans and standardizes the
laboratory results, and the validate_lab_result() function
performs validation to ensure the plausibility of these results. This
vignette aims to explain the theoretical background, usage, and
customization of these functions.
lab2clean packageYou can install and load the lab2clean package directly
in R.
#install.packages("lab2clean")After installation, load the package:
library(lab2clean)The clean_lab_result() has five arguments:
lab_data : A dataset containing laboratory
data
raw_result : The column in lab_data
that contains raw result values to be cleaned
locale : A string representing the locale for the
laboratory data. Defaults to “NO”
report : A report is written in the console.
Defaults to “TRUE”.
n_records : In case you are loading a grouped list
of distinct results, then you can assign the n_records to the column
that contains the frequency of each distinct result. Defaults to
NA
Let us demonstrate the clean_lab_result() function using
Function_1_dummy and inspect the first six rows:
data("Function_1_dummy", package = "lab2clean")
head(Function_1_dummy,6)| raw_result | frequency |
|---|---|
| ? | 108 |
| * | 243 |
| [ | 140 |
| _ | 268 |
| 1.1 x 10^9 | 284 |
| 2.34 x 10E12 | 42 |
This dataset -for demonstration purposes- contains two columns:
raw_result and the frequency. The
raw_result column holds raw laboratory results, and
frequency indicates how often each result appeared. Let’s
explore the report and n_records
arguments:
cleaned_results <- clean_lab_result(Function_1_dummy, raw_result = "raw_result", report = TRUE, n_records = "frequency")#> Step 1: Handling records with extra variables stored
with the result value removing interpretative flags, or
units
#>
==========================================================================================
#>
⚠ 8 distinct results (8.742% of the total result records)
with interpretative flags (e.g. positive, negative, H, L) -> flags
removed with cleaning comment added flag).
#> ⚠ 17 distinct results (20.043% of the total result records)
with unit (%, exponents, or other units) -> units removed with
cleaning comment added Percent, Exponent, or
Units).
#> Step 2: classify and
standardize different scale types - part 1
#>
==========================================================================================
#>
✔ 3 distinct results (5.373% of the total result records)
of scale type ‘Ord.2’, which
describes grades of positivity (e.g. 2+, 3+).
#> ✔ 7 distinct results (7.966% of the total result records)
of scale type ‘Qn.2’, which
describes inequality results (e.g. >120, <1).
#> ✔ 4 distinct results (6.233% of the total result records)
of scale type ‘Qn.3’, which
describes numeric range results (e.g. 2-4).
#> ✔ 4 distinct results (3.092% of the total result records)
of scale type ‘Qn.4’, which
describes titer results (e.g. 1/40).
#> ✔ 55 distinct results (61.335% of the total result records)
of scale type ‘Qn.1’, which
describes numeric results (e.g. 56, 5.6, 5600).
#> ⚠ 4 distinct results (4.853% of the total result records)
with numeric result values that cannot be determined without predefined
locale setting (US or DE) -> cleaning comment added locale_check).
#> ✔ 4 distinct results (4.888% of the total result records)
of scale type ‘Ord.1’, which
describes positive or negative results (Neg, Pos, or Normal).
#>
✔ 1 distinct results (1.019% of the total result records)
of scale type ‘Nom.1’, which
describes blood groups (e.g. A+, AB).
#> Last Step:
Classifying non-standard text records
#>
==========================================================================================
#>
⚠ 0 distinct results (0% of the total result records) with
multiple result values (e.g. postive X & negative Y) -> cleaning
comment added (multiple_results).
#> ⚠ 0 distinct results (0% of the total result records) with
words about sample or specimen (e.g. sample not found) -> cleaning
comment added (test_not_performed).
#> ⚠ 8 distinct results (8.777% of the total result records)
with meaningless inputs (e.g. = , .) -> cleaning comment added (No_result).
#> ⚠ 1 distinct results (1.317% of the total result records)
that could not be standardized or classified -> cleaning comment
added (not_standardized).
#>
==========================================================================================
#>
✔ 78 distinct results (89.906% of the total result records)
were cleaned, classified, and standardized.
#> ⏰ Time
taken is 0.012 minutes.
#>
The report provides a detailed report on how the whole
process of cleaning the data is done, and offers some descriptive
insights of the process. The n_records argument adds
percentages to each of the aforementioned steps to enhance the
reporting. For simplicity, we will use report = FALSE in
the rest of this tutorial:
cleaned_results <- clean_lab_result(Function_1_dummy, raw_result = "raw_result", report = FALSE)#> ✔ 78 result records were cleaned,
classified, and standardized.
#> ⏰ Time taken is 0.01 minutes.
#>
cleaned_results| raw_result | frequency | clean_result | scale_type | cleaning_comments |
|---|---|---|---|---|
| ? | 108 | NA | NA | No_result |
| * | 243 | NA | NA | No_result |
| [ | 140 | NA | NA | No_result |
| _ | 268 | NA | NA | No_result |
| 1.1 x 10^9 | 284 | 1.1 | Qn.1 | Exponents |
| 2.34 x 10E12 | 42 | 2.34 | Qn.1 | Exponents |
| 2,34 X 10^12 | 173 | 2.34 | Qn.1 | Exponents |
| 3.14159 * 10^30 | 271 | 3.142 | Qn.1 | Exponents |
| 1.1x10+9 | 179 | 1.1 | Qn.1 | Exponents |
| 2,34X10^12 | 153 | 2.34 | Qn.1 | Exponents |
| 3.14159*10^30 | 288 | 3.142 | Qn.1 | Exponents |
| 3.142*10^30 | 152 | 3.142 | Qn.1 | Exponents |
| 1,1 x 10e9 | 213 | 1.1 | Qn.1 | Exponents |
| 3 | 185 | 3 | Qn.1 | NA |
| 1.1 x 10^-9 | 58 | 1.1 | Qn.1 | Exponents |
| 2.34 X 10-12 | 273 | 2.34 | Qn.1 | Exponents |
| 3.14159E-30 | 96 | 3.142 | Qn.1 | Exponents |
| 1x10^9 | 41 | 1 | Qn.1 | Exponents |
| 1E9 | 119 | 1 | Qn.1 | Exponents |
| 2+ | 288 | 2+ | Ord.2 | NA |
| + | 270 | 1+ | Ord.2 | NA |
| +++ | 217 | 3+ | Ord.2 | NA |
| 0-1 | 203 | 0-1 | Qn.3 | NA |
| 1-2 | 298 | 1-2 | Qn.3 | NA |
| 1- | 207 | 1 | Qn.1 | flag |
| 01-02 | 221 | 1-2 | Qn.3 | NA |
| 1 -2 | 177 | 1-2 | Qn.3 | NA |
| 3 - 2 | 190 | NA | NA | not_standardized |
| - | 108 | Neg | Ord.1 | NA |
| + 230 | 70 | 230 | Qn.1 | flag |
| 100* | 290 | 100 | Qn.1 | NA |
| +56 | 274 | 56 | Qn.1 | flag |
| - 5 | 216 | 5 | Qn.1 | flag |
| 80% | 245 | 80 | Qn.1 | Percent |
| -5 | 37 | -5 | Qn.1 | NA |
| > 12 | 159 | >12 | Qn.2 | NA |
| <1050 | 235 | <1050 | Qn.2 | NA |
| < 02 | 88 | <2 | Qn.2 | NA |
| >= 20.3 | 116 | >=20.3 | Qn.2 | NA |
| >1:40 | 93 | >1:40 | Qn.4 | NA |
| 1/80 | 69 | 1:80 | Qn.4 | NA |
| <1/20 | 142 | <1:20 | Qn.4 | NA |
| < 1/020 | 142 | <1:020 | Qn.4 | NA |
| = | 130 | NA | NA | No_result |
| / | 71 | NA | NA | No_result |
| 0.2 | 67 | 0.2 | Qn.1 | NA |
| 33 Normal | 93 | 33 | Qn.1 | flag |
| negative 0.1 | 156 | 0.1 | Qn.1 | flag |
| H 256 | 102 | 256 | Qn.1 | flag |
| 30% | 262 | 30 | Qn.1 | Percent |
| 23 % | 42 | 23 | Qn.1 | Percent |
| 1056 | 149 | 1056 | Qn.1 | NA |
| 1056040 | 246 | 1056040 | Qn.1 | NA |
| 3560 | 63 | 3560 | Qn.1 | NA |
| 0,3 | 181 | 0.3 | Qn.1 | NA |
| 15,6 | 86 | 15.6 | Qn.1 | NA |
| 2.9 | 64 | 2.9 | Qn.1 | NA |
| 02.9 | 233 | 2.9 | Qn.1 | NA |
| 2.90 | 272 | 2.9 | Qn.1 | NA |
| 250 | 131 | 250 | Qn.1 | NA |
| 1.025 | 210 | 1.025 | Qn.1 | locale_check |
| 1.025 | 56 | 1.025 | Qn.1 | locale_check |
| 1025 | 134 | 1025 | Qn.1 | NA |
| 1025 | 104 | 1025 | Qn.1 | NA |
| 1025.7 | 250 | 1025.7 | Qn.1 | NA |
| 1.025,7 | 151 | 1025.7 | Qn.1 | NA |
| 1.025,36 | 249 | 1025.36 | Qn.1 | NA |
| 1,025.36 | 249 | 1025.36 | Qn.1 | NA |
| >1.025,36 | 244 | >1025.36 | Qn.2 | NA |
| <=1,025.36 | 149 | <=1025.36 | Qn.2 | NA |
| 1.015 | 234 | 1.015 | Qn.1 | locale_check |
| 1,060 | 200 | 1,060 | Qn.1 | locale_check |
| 2,5 | 222 | 2.5 | Qn.1 | NA |
| 2.5 | 30 | 2.5 | Qn.1 | NA |
| >3,48 | 158 | >3.48 | Qn.2 | NA |
| 3.48 | 89 | 3.48 | Qn.1 | NA |
| 93 | 133 | 93 | Qn.1 | NA |
| ,825 | 195 | 0.825 | Qn.1 | NA |
| 0,825 | 125 | 0.825 | Qn.1 | NA |
| 1.256894 | 60 | 1.257 | Qn.1 | NA |
| . | 96 | NA | NA | No_result |
| , | 210 | NA | NA | No_result |
| Négatif 0.3 | 143 | 0.3 | Qn.1 | flag |
| Négatif | 243 | Neg | Ord.1 | NA |
| Pøsitivo | 58 | Pos | Ord.1 | NA |
| A+ | 147 | A | Nom.1 | NA |
| pos & negative Y | 296 | Neg | Ord.1 | NA |
This function creates three different columns:
1- clean_result: The cleaned version of the
raw_result column. For example, “?” is converted to
2- scale_type : Categorizes the cleaned results into
specific types like Quantitative (Qn), Ordinal (Ord), or Nominal (Nom),
with further subcategories for nuanced differences, such as
differentiating simple numeric results (Qn.1) from inequalities (Qn.2),
range results (Qn.3), or titer results (Qn.4) within the Quantitative
scale.
3- cleaning_comments: Provides insights on how the
results were cleaned.
The process above provided a generic description on how the
clean_lab_result() function operates. It would be useful to
delve into more details on the exact way that some of the specific raw
results are cleaned:
Locale variable:In the clean_lab_result() function, we have an argument
named locale. It addresses the variations in number formats with
different decimal and thousand separators that arise due to
locale-specific settings used internationally. We chose to standardize
these varying languages and locale-specific settings to have the cleaned
results in English, US. If the user did not identify the locale of the
dataset, the default is NO, which means not specified. For
example for rows 71 and 72, there is a locale_check in the
cleaning_comments, and the results are 1.015 and 1,060
respectively. That means that either “US” or “DE” locale should be
specified to identify this result value. If we specified the locale as
US or DE, we can see different values as
follows:
Function_1_dummy_subset <- Function_1_dummy[c(71,72),, drop = FALSE]
cleaned_results <- clean_lab_result(Function_1_dummy_subset, raw_result = "raw_result", report = FALSE, locale = "US")#> ✔ 2 result records were cleaned,
classified, and standardized.
#> ⏰ Time taken is 0.007 minutes.
#>
cleaned_results| raw_result | frequency | clean_result | scale_type | cleaning_comments | |
|---|---|---|---|---|---|
| 71 | 1.015 | 234 | 1.015 | Qn.1 | |
| 72 | 1,060 | 200 | 1060 | Qn.1 |
cleaned_results <- clean_lab_result(Function_1_dummy_subset, raw_result = "raw_result", report = FALSE, locale = "DE")#> ✔ 2 result records were cleaned,
classified, and standardized.
#> ⏰ Time taken is 0.008 minutes.
#>
cleaned_results| raw_result | frequency | clean_result | scale_type | cleaning_comments | |
|---|---|---|---|---|---|
| 71 | 1.015 | 234 | 1015 | Qn.1 | |
| 72 | 1,060 | 200 | 1.06 | Qn.1 |
Language in common words:In the clean_lab_result() function, we support 19
distinct languages in representing frequently used terms such as “high,”
“low,” “positive,” and “negative. For example, the word
Pøsitivo is included in the common words and will be
cleaned as Pos.
Let us see how this data table works in our function:
data("common_words", package = "lab2clean")
common_words| Language | Positive | Negative | Not_detected | High | Low | Normal | Sample | Specimen |
|---|---|---|---|---|---|---|---|---|
| English | Positive | Negative | Not detected | High | Low | Normal | Sample | Specimen |
| Spanish | Positivo | Negativo | No detectado | Alto | Bajo | Normal | Muestra | Especimen |
| Portuguese | Positivo | Negativo | Nao detectado | Alto | Baixo | Normal | Amostra | Especime |
| French | Positif | Negatif | Non detecte | Eleve | Bas | Normal | Echantillon | Specimen |
| German | Positiv | Negativ | Nicht erkannt | Hoch | Niedrig | Normal | Probe | Probe |
| Italian | Positivo | Negativo | Non rilevato | Alto | Basso | Normale | Campione | Campione |
| Dutch | Positief | Negatief | Niet gedetecteerd | Hoog | Laag | Normaal | Staal | Monster |
| Polish | Dodatni | Ujemny | Nie wykryto | Wysoki | Niski | Normalny | Probka | Probka |
| Swedish | Positiv | Negativ | Inte upptackt | Hog | Lag | Normal | Prov | Prov |
| Danish | Positiv | Negativ | Ikke opdaget | Hoj | Lav | Normal | Prove | Prove |
| Norwegian | Positiv | Negativ | Ikke oppdaget | Hoy | Lav | Normal | Prove | Prove |
| Finnish | Positiivinen | Negatiivinen | Ei havaittu | Korkea | Matala | Normaali | Nayte | Nayte |
| Czech | Pozitivni | Negativni | Nezjisteno | Vysoky | Nizky | Normalni | Vzorek | Vzorek |
| Hungarian | Pozitiv | Negativ | Nem eszlelt | Magas | Alacsony | Normal | Mintavetel | Mintadarab |
| Croatian | Pozitivan | Negativan | Nije otkriveno | Visok | Nizak | Normalan | Uzorak | Uzorak |
| Slovak | Pozitivny | Negativny | Nezistene | Vysoky | Nizky | Normalny | Vzorka | Vzorka |
| Slovenian | Pozitiven | Negativen | Ni zaznano | Visok | Nizek | Normalno | Vzorec | Vzorec |
| Estonian | Positiivne | Negatiivne | Ei tuvastatud | Korge | Madal | Normaalne | Proov | Proov |
| Lithuanian | Teigiamas | Neigiamas | Neaptiktas | Aukstas | Zemas | Normalus | Pavyzdys | Pavyzdys |
As seen in this data, there are 19 languages for 8 common words. If
the words are positive or negative, then the result will either be
cleaned to Pos or Neg unless if it is
proceeded by a number, therefore the word is removed and a flag is added
to the cleaning_comments. For example, the word
Négatif 0.3 is cleaned as 0.3 and the word
33 Normal is cleaned as 33. Finally, if the
result has one of those words “Sample” or “Specimen”, then a comment
will pop-up mentioning that test was not performed.
Flag creation:In addition to the common words, when there is a space between a
numeric value and a minus character, this also creates a flag. For
example, result - 5 is cleaned as 5 with a
flag, but the result -5 is cleaned as -5, and
no flag is created because we can assume it was a negative value.
The validate_lab_result() has six arguments:
lab_data : A data frame containing laboratory
data
result_value : The column in lab_data with
quantitative result values for validation
result_unit : The column in lab_data with result
units in a UCUM-valid format
loinc_code : The column in lab_data indicating the
LOINC code of the laboratory test
patient_id : The column in lab_data indicating the
identifier of the tested patient.
lab_datetime : The column in lab_data with the date
or datetime of the laboratory test.
report : A report is written in the console.
Defaults to “TRUE”.
Let us check how our package validates the results using the
validate_lab_result() function. Let us consider the
Function_2_dummy data that contains 86,863 rows and inspect
its first 6 rows;
data("Function_2_dummy", package = "lab2clean")
head(Function_2_dummy, 6)| patient_id | lab_datetime1 | loinc_code | result_value | result_unit |
|---|---|---|---|---|
| 10000003 | 2023-08-09 | 1975-2 | 19 | umol/L |
| 10000003 | 2023-08-09 | 1968-7 | 20 | umol/L |
| 10000003 | 2023-09-09 | 1975-2 | 19 | mmol/L |
| 10000003 | 2023-09-09 | 1968-7 | 20 | umol/L |
| 10000003 | 2023-09-09 | 1968-7 | 20 | umol/L |
| 10000011 | 2023-10-09 | 1975-2 | 19 | umol/L |
Let us apply the validate_lab_result() and see its
functionality:
validate_results <- validate_lab_result(Function_2_dummy,
result_value="result_value",
result_unit="result_unit",
loinc_code="loinc_code",
patient_id = "patient_id" ,
lab_datetime="lab_datetime1")#> Preprocessing Step for Duplicate
Records
#>
===============================================================================================
#>
⚠ 166 duplicate records were
flagged.
#> These are multiple records of the same test for the
same patient at the same result timestamp.
#> Check 1:
Reportable Limits Check
#>
===============================================================================================
#>
⚠ 5 extremely low result records were
flagged (low_unreportable).
#> ⚠ 2 extremely high records were
flagged (high_unreportable).
#> Check 2: Logic Consistency
Checks
#>
===============================================================================================
#>
⚠ 7 result records were flagged for
violating relational logic rules (logic_flag).
#> Check 3:
Delta Change Limits Checks
#>
===============================================================================================
#>
⚠ 55 records were flagged for having
extreme change values from previous results within 7 days
(delta_flag_7d).
#> ⚠
15 records were flagged for
having extreme change values from previous results within 8-90 days
(delta_flag_8_90d).
#>
===============================================================================================
#>
✔ 99.712% of the lab data records were
validated with no flag detected.
#> ⏰ Time taken is 1.596 minutes.
#>
The validate_lab_result() function generates a
flag column, with different checks:
head(validate_results, 6)| loinc_code | result_unit | patient_id | lab_datetime1 | result_value | flag |
|---|---|---|---|---|---|
| 13457-7 | mg/dL | 1e+07 | 2023-09-09 | 100.0 | NA |
| 13457-7 | mg/dL | 1e+07 | 2023-10-09 | 100.0 | logic_flag |
| 1751-7 | g/dl | 1e+07 | 2023-08-09 | 3.1 | NA |
| 1751-7 | g/dl | 1e+07 | 2023-09-09 | 7.5 | logic_flag |
| 1751-7 | g/dl | 1e+07 | 2023-10-09 | 7.5 | NA |
| 18262-6 | mg/dL | 1e+07 | 2023-11-09 | 100.0 | NA |
levels(factor(validate_results$flag))#> [1] “delta_flag_7d” “delta_flag_8_90d” “duplicate”
#>
[4] “high_unreportable” “logic_flag” “low_unreportable”
We can now subset specific patients to explain the flags:
subset_patients <- validate_results[validate_results$patient_id %in% c("14236258", "10000003", "14499007"), ]
subset_patients| loinc_code | result_unit | patient_id | lab_datetime1 | result_value | flag |
|---|---|---|---|---|---|
| 13457-7 | mg/dL | 10000003 | 2023-09-09 | 100.0 | NA |
| 13457-7 | mg/dL | 10000003 | 2023-10-09 | 100.0 | logic_flag |
| 1751-7 | g/dl | 10000003 | 2023-08-09 | 3.1 | NA |
| 1751-7 | g/dl | 10000003 | 2023-09-09 | 7.5 | logic_flag |
| 1751-7 | g/dl | 10000003 | 2023-10-09 | 7.5 | NA |
| 18262-6 | mg/dL | 10000003 | 2023-11-09 | 100.0 | NA |
| 1968-7 | umol/L | 10000003 | 2023-08-09 | 20.0 | logic_flag |
| 1968-7 | umol/L | 10000003 | 2023-09-09 | 20.0 | duplicate |
| 1968-7 | umol/L | 10000003 | 2023-09-09 | 20.0 | duplicate |
| 1968-7 | umol/L | 10000003 | 2023-10-09 | 20.0 | NA |
| 1975-2 | umol/L | 10000003 | 2023-08-09 | 19.0 | logic_flag |
| 1975-2 | mmol/L | 10000003 | 2023-09-09 | 19.0 | NA |
| 2085-9 | mg/dL | 10000003 | 2023-09-09 | 130.0 | NA |
| 2085-9 | mg/dL | 10000003 | 2023-10-09 | 130.0 | logic_flag |
| 2085-9 | mg/dL | 10000003 | 2023-11-09 | 130.0 | NA |
| 2093-3 | mg/dL | 10000003 | 2023-08-09 | 230.0 | NA |
| 2093-3 | mg/dL | 10000003 | 2023-09-09 | 230.0 | duplicate |
| 2093-3 | mg/dL | 10000003 | 2023-09-09 | 215.0 | duplicate |
| 2093-3 | mg/dL | 10000003 | 2023-10-09 | 230.0 | logic_flag |
| 2093-3 | ng/dL | 10000003 | 2023-11-09 | 230.0 | NA |
| 2885-2 | g/dl | 10000003 | 2023-08-09 | 7.0 | NA |
| 2885-2 | g/dl | 10000003 | 2023-09-09 | 7.0 | logic_flag |
| 2885-2 | mg/dl | 10000003 | 2023-10-09 | 7.0 | NA |
| 2160-0 | mg/dL | 14236258 | 2180-11-23 22:30:00 | 13.2 | NA |
| 2160-0 | mg/dL | 14236258 | 2181-02-22 08:10:00 | 13.1 | NA |
| 2160-0 | mg/dL | 14236258 | 2181-03-07 11:00:00 | 9.4 | NA |
| 2160-0 | mg/dL | 14236258 | 2181-03-24 16:35:00 | 27.2 | delta_flag_8_90d |
| 2160-0 | mg/dL | 14236258 | 2181-03-25 06:25:00 | 16.8 | delta_flag_7d |
| 2160-0 | mg/dL | 14236258 | 2181-03-26 06:10:00 | 19.0 | NA |
| 2160-0 | mg/dL | 14236258 | 2181-04-02 10:00:00 | 9.7 | delta_flag_7d |
| 2160-0 | mg/dL | 14236258 | 2181-06-29 14:00:00 | 16.9 | delta_flag_8_90d |
| 2160-0 | mg/dL | 14236258 | 2181-06-30 05:32:00 | 10.8 | delta_flag_7d |
| 2160-0 | mg/dL | 14236258 | 2181-07-10 22:44:00 | 10.0 | NA |
| 2160-0 | mg/dL | 14236258 | 2181-07-10 23:25:00 | 10.3 | NA |
| 2160-0 | mg/dL | 14236258 | 2181-07-11 10:00:00 | 11.6 | NA |
| 2160-0 | mg/dL | 14236258 | 2181-07-12 02:30:00 | 13.6 | NA |
| 2160-0 | mg/dL | 14236258 | 2181-10-17 17:10:00 | 10.6 | NA |
| 2160-0 | mg/dL | 14236258 | 2181-10-18 06:40:00 | 12.6 | NA |
| 2160-0 | mg/dL | 14236258 | 2181-11-30 07:00:00 | 19.7 | delta_flag_8_90d |
| 2160-0 | mg/dL | 14236258 | 2181-12-17 06:44:00 | 12.1 | delta_flag_8_90d |
| 2160-0 | mg/dL | 14499007 | 2180-06-02 07:10:00 | 1.0 | NA |
| 2160-0 | mg/dL | 14499007 | 2180-10-26 15:00:00 | 0.8 | NA |
| 2160-0 | mg/dL | 14499007 | 2180-10-27 05:53:00 | 1.0 | NA |
| 2160-0 | mg/dL | 14499007 | 2180-10-27 15:15:00 | 0.0 | low_unreportable |
| 2160-0 | mg/dL | 14499007 | 2180-10-28 06:35:00 | 0.9 | NA |
| 2160-0 | mg/dL | 14499007 | 2180-10-29 05:52:00 | 1.0 | NA |
| 2160-0 | mg/dL | 14499007 | 2180-10-30 12:26:00 | 0.9 | NA |
| 2160-0 | mg/dL | 14499007 | 2180-10-31 03:11:00 | 0.8 | NA |
| 2160-0 | mg/dL | 14499007 | 2180-11-01 06:20:00 | 1.0 | NA |
| 2160-0 | mg/dL | 14499007 | 2180-11-02 04:22:00 | 1.0 | NA |
delta_flag_8_90d and
delta_flag_7d that is calculated by lower and upper
percentiles set to 0.0005 and 0.9995 respectively. While the delta check
is effective in identifying potentially erroneous result values, we
acknowledge that it may also flag clinically relevant changes.
Therefore, it is crucial that users interpret these flagged results in
conjunction with the patient’s clinical context.Let us also explain two tables that we used for the validation function. Let us begin with the reportable interval table.
data("reportable_interval", package = "lab2clean")
reportable_interval_subset <- reportable_interval[reportable_interval$interval_loinc_code == "2160-0", ]
reportable_interval_subset| interval_loinc_code | UCUM_unit | low_reportable_limit | high_reportable_limit |
|---|---|---|---|
| 2160-0 | mg/dL | 1e-04 | 120 |
low_unreportable. As
we can see, for the “2160-0” loinc_code, his result was 0.0 which was
not in the reportable range (0.0001, 120). In a similar note, patient
17726236 has a high_unreportable.Logic rules ensure that related test results are consistent:
data("logic_rules", package = "lab2clean")
logic_rules <- logic_rules[logic_rules$rule_id == 3, ]
logic_rules| rule_id | rule_index | rule_part | rule_part_type |
|---|---|---|---|
| 3 | 1 | 2093-3 | loinc_code |
| 3 | 2 | >( | operator |
| 3 | 3 | 2085-9 | loinc_code |
| 3 | 4 | + | operator |
| 3 | 5 | 13457-7 | loinc_code |
| 3 | 6 | ) | operator |
logic_flag and
duplicate. The duplicate means that this
patient has a duplicate row, whereas the logic_flag should
be interpreted as follows. For the loinc_code “2093-3”, which is
cholesterol, we need that the “2093-3” > “2085-9” + “13457-7”, or
equivalently cholesterol > hdl cholesterol + ldl cholesterol (from
the logic rules table). Therefore for patient 10000003, we have a logic
flag because LDL (“13457-7”) equals 100.0 and HDL (“2085-9”) equals
130.0. Total cholesterol (“2093-3”) equals 230. Therefore we see that
the rule “2093-3” > “2085-9” + “13457-7” is not satisfied because we
have 230 > 100+130, i.e. 230>230, which is clearly false, and thus
a logic flag is created.We fully acknowledge the importance of customization to accommodate
diverse user needs and tailor the functions to specific datasets. To
this end, the data in logic_rules,
reportable_interval, and common_words are not
hard-coded within the function scripts but are instead provided as
separate data files in the “data” folder of the package. This approach
allows users to benefit from the default data we have included, which
reflects our best knowledge, while also providing the flexibility to
append or modify the data as needed.
For example, users can easily customize the common_words
RData file by adding phrases that are used across different languages
and laboratory settings. This allows the clean_lab_result()
function to better accommodate the specific linguistic and contextual
nuances of their datasets. Similarly, users can adjust the
logic_rules and reportable_interval data files
for validate_lab_result() function to reflect the unique
requirements or standards of their research or clinical environment.
By providing these customizable data files, we aim to ensure that the
lab2clean package is not only powerful but also adaptable
to the varied needs of the research and clinical communities.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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