README.md
In mcarmonabaez/icd10es: Classification of diseases, symptoms, injuries, and other health-related issues
icd10es - A user-friendly R package for disease clasification in Spanish š²š½
Table of Contents
Package description š·
icd10es is an R package created for Spanish-speaking Bioinformatics
specialists š©āāļø who have to deal with classifying written descriptions
of diseases, symptoms, and injuries, among other health-related issues,
in the 10th edition of the International Statistical Classification of
Diseases and Related Health Problems (ICD-10 for short), referred to as
CIE-10 in Spanish. āļø
Installing icd10es
devtools::install_github("mcarmonabaez/icd10es")
Congratulations! Now you can use this package! š
Features at a glance
Printing information of a CIE-10 entry
Letās start with a simple task: say you wish to know what the entry
āA00.0ā in the catalog contains. The function printInfo can help
with that. Changing the value of the parameter tabular you can decide
whether you want to
-
get only the canonical term in table form,
-
get the canonical and all inclusion terms (if they exist) also in
table form,
-
print in all associated information in the console for quick
inquiries.
library(icd10es)
printInfo('S72.1', tabular = 'single')
#> category subcategory disease
#> 1 S72 S721 fractura pertrocanteriana
printInfo('S72.1', tabular = 'simple')
#> category subcategory disease term
#> 1 S72 S721 fractura pertrocanteriana Canonical
#> 2 S72 S721 fractura intertrocanteriana Inclusion
#> 3 S72 S721 fractura trocanteriana Inclusion
printInfo('S72.1', tabular = 'full')
#> -----------------------------------------------------------------------------
#> Query entered: S72.1
#> Cateogry: S72 fractura del femur
#> Subcateogry: S72.1 fractura pertrocanteriana
#> -----------------------------------------------------------------------------
#> The following inclusion terms were found:
#> fractura intertrocanteriana
#> fractura trocanteriana
#> -----------------------------------------------------------------------------
Looking up a string in the catalog š
The main function of icd10es consists in entering a string which is
expected to match some entry in the CIE-10 and finding said entry, all
via the ICDLookUp function. The string does not have to be identical
to the entry: herein lies the usefulness of the package.
The function first tries to find an exact match in the catalog, but
often it occurs that the string either has a typo of some kind
(e.g.Ā writing āpnuemoniaā instead of āpneumonia) or uses a more
colloquial way of referring to the disease or symptom and is not its
āfull nameā. When this happens, the function tries fuzzy matching
using the Jaro-Winkler similarity metric.
For example, in the CIE-10, all cancers are referred to in a more formal
way, such as ātumor maligno del colonā instead of ācancer de colonā (in
English: āmalignant neoplasm of colonā instead of ācolon cancerā).
ICDLookUp would give the following output:
ICDLookUp('cancer de colon', tabular = 'simple')
#> category subcategory disease term
#> 1 C18 C189 tumor maligno del colon parte no especificada Canonical
#> 2 C18 C189 tumor maligno del intestino grueso sai Inclusion
Note how the tabular parameter is inherited to printInfo.
When doing fuzzy matching, one can be more or less strict. This is
reflected in the jwBound parameter of ICDLookUp: the Jaro-Winkler
similarity goes from 0 (no similarity) to 1 (exact match), and the
default value of jwBound is 0.9. That is, only entries with a
similarity to the entered string equal or higher than 0.9 will be
considered. But if one finds that the function didnāt find a result, one
can try lowering the bound:
ICDLookUp('sindrome dandie-waker', jwBound = 0.9, tabular = 'simple')
#> category subcategory disease
#> 1 <NA> <NA> <NA>
ICDLookUp('sindrome dandie-waker', jwBound = 0.8, tabular = 'simple')
#> category subcategory disease
#> 1 Q03 Q031 atresia de los agujeros de magendie y de luschka
#> 2 Q03 Q031 sindrome de dandywalker
#> term
#> 1 Canonical
#> 2 Inclusion
Using an external catalog
It can happen that the user wants to look up strings in a different,
specialized catalog. This could be for example when using an auxiliary
catalog which has alternative names of some diseases due to regional
variations (like when a country or a countryās province historically
calls a disease in a special way).
This can be done by making the ICDLookUp parameter useExternal =
TRUE, and by giving a dataframe to externalCatalog:
auxCatalog <- read.delim('https://raw.githubusercontent.com/mcarmonabaez/icd10es/master/inst/extdata/inputs/diabetes_subcategories.csv')
ICDLookUp('Diabetes tipo i con coma', tabular = 'simple',
useExternal = TRUE, externalCatalog = auxCatalog)
#> category subcategory
#> 1 E10 E100
#> 2 E10 E100
#> 3 E10 E100
#> 4 E10 E100
#> 5 E10 E100
#> 6 E10 E100
#> disease
#> 1 diabetes mellitus insulinodependiente con coma
#> 2 diabetes mellitus insulinodependiente con coma diabetico
#> 3 diabetes mellitus insulinodependiente con coma diabetico con o sin cetoacidosis
#> 4 diabetes mellitus insulinodependiente con coma diabetico hiperosmolar
#> 5 diabetes mellitus insulinodependiente con coma diabetico hipoglicemico
#> 6 diabetes mellitus insulinodependiente con coma diabetico hiperglicemico sai
#> term
#> 1 Canonical
#> 2 Inclusion
#> 3 Inclusion
#> 4 Inclusion
#> 5 Inclusion
#> 6 Inclusion
Looking up entries within death certificates š§Ŗ
It is very common to be in possession of longer texts that describe a
series of diseases and symptoms which could be matched to the CIE-10.
Some examples include death certificates or medical records. There, a
physician may list some or all comorbidities a person presents when
having a medical checkup or when passing away. One may then wish to
match all listed health-related problems with the CIE-10.
exampleCerificates <-
tibble::tribble(~id, ~cause,
1, 'HEMORRAGIA SUBARACNOIDEA. HIPERTENSION ARTERIAL SISTEMICA. DISLIPIDEMIA.',
2, 'INFARTO CEREBRAL, HIPERTENSION ARTERIAL SISTEMICA, TRIGLICERIDEMIA.',
3, 'HERIDA PRODUCIDA POR PROYECTIL DE ARMA DE FUEGO PENETRANTE DE TORAX.',
4, 'CHOQUE HIPOVOLEMICO, DIARREA CRONICA, INFECCION POR VIRUS DE INMUNODEFICIENCIA HUMANA.',
5, 'EVENTO VASCULAR CEREBRAL, ENFERMEDAD RENAL TERMINAL, DIABETES MELLITUS TIPO 2.',
6, 'ANEURISMA CEREBRAL, ENCEFALOPATIA HEPATICA.',
7, 'INFARTO AGUDO AL MIOCARDIO, CARDIOPATIA HIPERTENSIVA, HIPERTENSION ARTERIAL SISTEMICA',
8, 'MENINGIOMA, HIPERTENSION ARTERIAL SISTEMICA.',
9, 'ENCEFALOPATIA HEPATICA, CIRROSIS HEPATICA, ALCOHOLISMO CRONICO',
10, 'INFARTO AGUDO AL MIOCARDIO, DIABETES MELLITUS TIPO II.'
)
exampleCerificates
#> # A tibble: 10 x 2
#> id cause
#> <dbl> <chr>
#> 1 1 HEMORRAGIA SUBARACNOIDEA. HIPERTENSION ARTERIAL SISTEMICA. DISLIPIDEMIā¦
#> 2 2 INFARTO CEREBRAL, HIPERTENSION ARTERIAL SISTEMICA, TRIGLICERIDEMIA.
#> 3 3 HERIDA PRODUCIDA POR PROYECTIL DE ARMA DE FUEGO PENETRANTE DE TORAX.
#> 4 4 CHOQUE HIPOVOLEMICO, DIARREA CRONICA, INFECCION POR VIRUS DE INMUNODEFā¦
#> 5 5 EVENTO VASCULAR CEREBRAL, ENFERMEDAD RENAL TERMINAL, DIABETES MELLITUSā¦
#> 6 6 ANEURISMA CEREBRAL, ENCEFALOPATIA HEPATICA.
#> 7 7 INFARTO AGUDO AL MIOCARDIO, CARDIOPATIA HIPERTENSIVA, HIPERTENSION ARTā¦
#> 8 8 MENINGIOMA, HIPERTENSION ARTERIAL SISTEMICA.
#> 9 9 ENCEFALOPATIA HEPATICA, CIRROSIS HEPATICA, ALCOHOLISMO CRONICO
#> 10 10 INFARTO AGUDO AL MIOCARDIO, DIABETES MELLITUS TIPO II.
First, one would have to tokenize each entry in the certificate,
creating a long dataframe in the following way using
tokenizeCertificates:
tokenizedCerificates <- tokenizeCertificates(exampleCerificates)
print(tokenizedCerificates, n = Inf)
#> # A tibble: 25 x 3
#> id cause order
#> <dbl> <chr> <int>
#> 1 1 hemorragia subaracnoidea 1
#> 2 1 hipertension arterial sistemica 2
#> 3 1 dislipidemia 3
#> 4 2 infarto cerebral 1
#> 5 2 hipertension arterial sistemica 2
#> 6 2 trigliceridemia 3
#> 7 3 herida producida por proyectil de arma de fuego penetrante de toā¦ 1
#> 8 4 choque hipovolemico 1
#> 9 4 diarrea cronica 2
#> 10 4 infeccion por virus de inmunodeficiencia humana 3
#> 11 5 evento vascular cerebral 1
#> 12 5 enfermedad renal terminal 2
#> 13 5 diabetes mellitus tipo 2 3
#> 14 6 aneurisma cerebral 1
#> 15 6 encefalopatia hepatica 2
#> 16 7 infarto agudo al miocardio 1
#> 17 7 cardiopatia hipertensiva 2
#> 18 7 hipertension arterial sistemica 3
#> 19 8 meningioma 1
#> 20 8 hipertension arterial sistemica 2
#> 21 9 encefalopatia hepatica 1
#> 22 9 cirrosis hepatica 2
#> 23 9 alcoholismo cronico 3
#> 24 10 infarto agudo al miocardio 1
#> 25 10 diabetes mellitus tipo ii 2
One can then proceed to use ICDLookUp to try to find an entry in the
catalog for each of the entries in the certificate:
results <- lapply(unique(tokenizedCerificates$id),
function(x) {
print(x)
subset <- dplyr::filter(tokenizedCerificates, id == x)
lapply(subset$cause, ICDLookUp)
}) %>%
bind_rows(.id = 'id',) %>%
mutate(id = as.numeric(id)) %>%
arrange(id) %>%
group_by(id) %>%
mutate(order = row_number()) %>%
ungroup()
tokenizedCerificates$result <- results$disease
print(tokenizedCerificates, n = Inf)
#> # A tibble: 25 x 4
#> id cause order result
#> <dbl> <chr> <int> <chr>
#> 1 1 hemorragia subaracnoidea 1 hemorragia subaracnoidea no especā¦
#> 2 1 hipertension arterial sistemiā¦ 2 hipertension esencial
#> 3 1 dislipidemia 3 trastornos del metabolismo de lasā¦
#> 4 2 infarto cerebral 1 infarto cerebral no especificado
#> 5 2 hipertension arterial sistemiā¦ 2 hipertension esencial
#> 6 2 trigliceridemia 3 trastornos del metabolismo de lasā¦
#> 7 3 herida producida por proyectiā¦ 1 <NA>
#> 8 4 choque hipovolemico 1 choque hipovolemico
#> 9 4 diarrea cronica 2 <NA>
#> 10 4 infeccion por virus de inmunoā¦ 3 enfermedad por virus de la inmunoā¦
#> 11 5 evento vascular cerebral 1 infarto cerebral no especificado
#> 12 5 enfermedad renal terminal 2 <NA>
#> 13 5 diabetes mellitus tipo 2 3 diabetes mellitus no insulinodepeā¦
#> 14 6 aneurisma cerebral 1 hemorragia subaracnoidea no especā¦
#> 15 6 encefalopatia hepatica 2 insuficiencia hepatica no clasifiā¦
#> 16 7 infarto agudo al miocardio 1 infarto agudo del miocardio
#> 17 7 cardiopatia hipertensiva 2 enfermedad cardiaca hipertensiva
#> 18 7 hipertension arterial sistemiā¦ 3 hipertension esencial
#> 19 8 meningioma 1 tumor benigno de las meninges parā¦
#> 20 8 hipertension arterial sistemiā¦ 2 hipertension esencial
#> 21 9 encefalopatia hepatica 1 insuficiencia hepatica no clasifiā¦
#> 22 9 cirrosis hepatica 2 cirrosis hepatica alcoholica
#> 23 9 alcoholismo cronico 3 sindrome de dependencia al alcohol
#> 24 10 infarto agudo al miocardio 1 infarto agudo del miocardio
#> 25 10 diabetes mellitus tipo ii 2 diabetes mellitus no insulinodepeā¦
License
This package is made available under the MIT License.
Credits
This package is created and maintained by Mariana Carmona-Baez and Juan
Bernardo MartĆnez Parente-CastaƱeda. š
Weāre open to suggestions, feel free to message us on
mcarmonabaez@gmail.com and jbmpc@outlook.com. Pull requests are
also welcome! š
Thanks
Thanks to Christopher Ormsby for his input and for letting us be part of
this awesome project :hospital:
Thanks to Teresa Ortiz for her invaluable
guidance :crystal_ball:
mcarmonabaez/icd10es documentation built on June 16, 2021, 11:24 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
icd10es - A user-friendly R package for disease clasification in Spanish š²š½
Table of Contents
Package description š·
icd10es is an R package created for Spanish-speaking Bioinformatics
specialists š©āāļø who have to deal with classifying written descriptions
of diseases, symptoms, and injuries, among other health-related issues,
in the 10th edition of the International Statistical Classification of
Diseases and Related Health Problems (ICD-10 for short), referred to as
CIE-10 in Spanish. āļø
Installing icd10es
devtools::install_github("mcarmonabaez/icd10es")
Congratulations! Now you can use this package! š
Features at a glance
Printing information of a CIE-10 entry
Letās start with a simple task: say you wish to know what the entry
āA00.0ā in the catalog contains. The function printInfo can help
with that. Changing the value of the parameter tabular you can decide
whether you want to
-
get only the canonical term in table form,
-
get the canonical and all inclusion terms (if they exist) also in table form,
-
print in all associated information in the console for quick inquiries.
library(icd10es)
printInfo('S72.1', tabular = 'single')
#> category subcategory disease
#> 1 S72 S721 fractura pertrocanteriana
printInfo('S72.1', tabular = 'simple')
#> category subcategory disease term
#> 1 S72 S721 fractura pertrocanteriana Canonical
#> 2 S72 S721 fractura intertrocanteriana Inclusion
#> 3 S72 S721 fractura trocanteriana Inclusion
printInfo('S72.1', tabular = 'full')
#> -----------------------------------------------------------------------------
#> Query entered: S72.1
#> Cateogry: S72 fractura del femur
#> Subcateogry: S72.1 fractura pertrocanteriana
#> -----------------------------------------------------------------------------
#> The following inclusion terms were found:
#> fractura intertrocanteriana
#> fractura trocanteriana
#> -----------------------------------------------------------------------------
Looking up a string in the catalog š
The main function of icd10es consists in entering a string which is
expected to match some entry in the CIE-10 and finding said entry, all
via the ICDLookUp function. The string does not have to be identical
to the entry: herein lies the usefulness of the package.
The function first tries to find an exact match in the catalog, but often it occurs that the string either has a typo of some kind (e.g.Ā writing āpnuemoniaā instead of āpneumonia) or uses a more colloquial way of referring to the disease or symptom and is not its āfull nameā. When this happens, the function tries fuzzy matching using the Jaro-Winkler similarity metric.
For example, in the CIE-10, all cancers are referred to in a more formal
way, such as ātumor maligno del colonā instead of ācancer de colonā (in
English: āmalignant neoplasm of colonā instead of ācolon cancerā).
ICDLookUp would give the following output:
ICDLookUp('cancer de colon', tabular = 'simple')
#> category subcategory disease term
#> 1 C18 C189 tumor maligno del colon parte no especificada Canonical
#> 2 C18 C189 tumor maligno del intestino grueso sai Inclusion
Note how the tabular parameter is inherited to printInfo.
When doing fuzzy matching, one can be more or less strict. This is
reflected in the jwBound parameter of ICDLookUp: the Jaro-Winkler
similarity goes from 0 (no similarity) to 1 (exact match), and the
default value of jwBound is 0.9. That is, only entries with a
similarity to the entered string equal or higher than 0.9 will be
considered. But if one finds that the function didnāt find a result, one
can try lowering the bound:
ICDLookUp('sindrome dandie-waker', jwBound = 0.9, tabular = 'simple')
#> category subcategory disease
#> 1 <NA> <NA> <NA>
ICDLookUp('sindrome dandie-waker', jwBound = 0.8, tabular = 'simple')
#> category subcategory disease
#> 1 Q03 Q031 atresia de los agujeros de magendie y de luschka
#> 2 Q03 Q031 sindrome de dandywalker
#> term
#> 1 Canonical
#> 2 Inclusion
Using an external catalog
It can happen that the user wants to look up strings in a different, specialized catalog. This could be for example when using an auxiliary catalog which has alternative names of some diseases due to regional variations (like when a country or a countryās province historically calls a disease in a special way).
This can be done by making the ICDLookUp parameter useExternal =
TRUE, and by giving a dataframe to externalCatalog:
auxCatalog <- read.delim('https://raw.githubusercontent.com/mcarmonabaez/icd10es/master/inst/extdata/inputs/diabetes_subcategories.csv')
ICDLookUp('Diabetes tipo i con coma', tabular = 'simple',
useExternal = TRUE, externalCatalog = auxCatalog)
#> category subcategory
#> 1 E10 E100
#> 2 E10 E100
#> 3 E10 E100
#> 4 E10 E100
#> 5 E10 E100
#> 6 E10 E100
#> disease
#> 1 diabetes mellitus insulinodependiente con coma
#> 2 diabetes mellitus insulinodependiente con coma diabetico
#> 3 diabetes mellitus insulinodependiente con coma diabetico con o sin cetoacidosis
#> 4 diabetes mellitus insulinodependiente con coma diabetico hiperosmolar
#> 5 diabetes mellitus insulinodependiente con coma diabetico hipoglicemico
#> 6 diabetes mellitus insulinodependiente con coma diabetico hiperglicemico sai
#> term
#> 1 Canonical
#> 2 Inclusion
#> 3 Inclusion
#> 4 Inclusion
#> 5 Inclusion
#> 6 Inclusion
Looking up entries within death certificates š§Ŗ
It is very common to be in possession of longer texts that describe a series of diseases and symptoms which could be matched to the CIE-10. Some examples include death certificates or medical records. There, a physician may list some or all comorbidities a person presents when having a medical checkup or when passing away. One may then wish to match all listed health-related problems with the CIE-10.
exampleCerificates <-
tibble::tribble(~id, ~cause,
1, 'HEMORRAGIA SUBARACNOIDEA. HIPERTENSION ARTERIAL SISTEMICA. DISLIPIDEMIA.',
2, 'INFARTO CEREBRAL, HIPERTENSION ARTERIAL SISTEMICA, TRIGLICERIDEMIA.',
3, 'HERIDA PRODUCIDA POR PROYECTIL DE ARMA DE FUEGO PENETRANTE DE TORAX.',
4, 'CHOQUE HIPOVOLEMICO, DIARREA CRONICA, INFECCION POR VIRUS DE INMUNODEFICIENCIA HUMANA.',
5, 'EVENTO VASCULAR CEREBRAL, ENFERMEDAD RENAL TERMINAL, DIABETES MELLITUS TIPO 2.',
6, 'ANEURISMA CEREBRAL, ENCEFALOPATIA HEPATICA.',
7, 'INFARTO AGUDO AL MIOCARDIO, CARDIOPATIA HIPERTENSIVA, HIPERTENSION ARTERIAL SISTEMICA',
8, 'MENINGIOMA, HIPERTENSION ARTERIAL SISTEMICA.',
9, 'ENCEFALOPATIA HEPATICA, CIRROSIS HEPATICA, ALCOHOLISMO CRONICO',
10, 'INFARTO AGUDO AL MIOCARDIO, DIABETES MELLITUS TIPO II.'
)
exampleCerificates
#> # A tibble: 10 x 2
#> id cause
#> <dbl> <chr>
#> 1 1 HEMORRAGIA SUBARACNOIDEA. HIPERTENSION ARTERIAL SISTEMICA. DISLIPIDEMIā¦
#> 2 2 INFARTO CEREBRAL, HIPERTENSION ARTERIAL SISTEMICA, TRIGLICERIDEMIA.
#> 3 3 HERIDA PRODUCIDA POR PROYECTIL DE ARMA DE FUEGO PENETRANTE DE TORAX.
#> 4 4 CHOQUE HIPOVOLEMICO, DIARREA CRONICA, INFECCION POR VIRUS DE INMUNODEFā¦
#> 5 5 EVENTO VASCULAR CEREBRAL, ENFERMEDAD RENAL TERMINAL, DIABETES MELLITUSā¦
#> 6 6 ANEURISMA CEREBRAL, ENCEFALOPATIA HEPATICA.
#> 7 7 INFARTO AGUDO AL MIOCARDIO, CARDIOPATIA HIPERTENSIVA, HIPERTENSION ARTā¦
#> 8 8 MENINGIOMA, HIPERTENSION ARTERIAL SISTEMICA.
#> 9 9 ENCEFALOPATIA HEPATICA, CIRROSIS HEPATICA, ALCOHOLISMO CRONICO
#> 10 10 INFARTO AGUDO AL MIOCARDIO, DIABETES MELLITUS TIPO II.
First, one would have to tokenize each entry in the certificate,
creating a long dataframe in the following way using
tokenizeCertificates:
tokenizedCerificates <- tokenizeCertificates(exampleCerificates)
print(tokenizedCerificates, n = Inf)
#> # A tibble: 25 x 3
#> id cause order
#> <dbl> <chr> <int>
#> 1 1 hemorragia subaracnoidea 1
#> 2 1 hipertension arterial sistemica 2
#> 3 1 dislipidemia 3
#> 4 2 infarto cerebral 1
#> 5 2 hipertension arterial sistemica 2
#> 6 2 trigliceridemia 3
#> 7 3 herida producida por proyectil de arma de fuego penetrante de toā¦ 1
#> 8 4 choque hipovolemico 1
#> 9 4 diarrea cronica 2
#> 10 4 infeccion por virus de inmunodeficiencia humana 3
#> 11 5 evento vascular cerebral 1
#> 12 5 enfermedad renal terminal 2
#> 13 5 diabetes mellitus tipo 2 3
#> 14 6 aneurisma cerebral 1
#> 15 6 encefalopatia hepatica 2
#> 16 7 infarto agudo al miocardio 1
#> 17 7 cardiopatia hipertensiva 2
#> 18 7 hipertension arterial sistemica 3
#> 19 8 meningioma 1
#> 20 8 hipertension arterial sistemica 2
#> 21 9 encefalopatia hepatica 1
#> 22 9 cirrosis hepatica 2
#> 23 9 alcoholismo cronico 3
#> 24 10 infarto agudo al miocardio 1
#> 25 10 diabetes mellitus tipo ii 2
One can then proceed to use ICDLookUp to try to find an entry in the
catalog for each of the entries in the certificate:
results <- lapply(unique(tokenizedCerificates$id),
function(x) {
print(x)
subset <- dplyr::filter(tokenizedCerificates, id == x)
lapply(subset$cause, ICDLookUp)
}) %>%
bind_rows(.id = 'id',) %>%
mutate(id = as.numeric(id)) %>%
arrange(id) %>%
group_by(id) %>%
mutate(order = row_number()) %>%
ungroup()
tokenizedCerificates$result <- results$disease
print(tokenizedCerificates, n = Inf)
#> # A tibble: 25 x 4
#> id cause order result
#> <dbl> <chr> <int> <chr>
#> 1 1 hemorragia subaracnoidea 1 hemorragia subaracnoidea no especā¦
#> 2 1 hipertension arterial sistemiā¦ 2 hipertension esencial
#> 3 1 dislipidemia 3 trastornos del metabolismo de lasā¦
#> 4 2 infarto cerebral 1 infarto cerebral no especificado
#> 5 2 hipertension arterial sistemiā¦ 2 hipertension esencial
#> 6 2 trigliceridemia 3 trastornos del metabolismo de lasā¦
#> 7 3 herida producida por proyectiā¦ 1 <NA>
#> 8 4 choque hipovolemico 1 choque hipovolemico
#> 9 4 diarrea cronica 2 <NA>
#> 10 4 infeccion por virus de inmunoā¦ 3 enfermedad por virus de la inmunoā¦
#> 11 5 evento vascular cerebral 1 infarto cerebral no especificado
#> 12 5 enfermedad renal terminal 2 <NA>
#> 13 5 diabetes mellitus tipo 2 3 diabetes mellitus no insulinodepeā¦
#> 14 6 aneurisma cerebral 1 hemorragia subaracnoidea no especā¦
#> 15 6 encefalopatia hepatica 2 insuficiencia hepatica no clasifiā¦
#> 16 7 infarto agudo al miocardio 1 infarto agudo del miocardio
#> 17 7 cardiopatia hipertensiva 2 enfermedad cardiaca hipertensiva
#> 18 7 hipertension arterial sistemiā¦ 3 hipertension esencial
#> 19 8 meningioma 1 tumor benigno de las meninges parā¦
#> 20 8 hipertension arterial sistemiā¦ 2 hipertension esencial
#> 21 9 encefalopatia hepatica 1 insuficiencia hepatica no clasifiā¦
#> 22 9 cirrosis hepatica 2 cirrosis hepatica alcoholica
#> 23 9 alcoholismo cronico 3 sindrome de dependencia al alcohol
#> 24 10 infarto agudo al miocardio 1 infarto agudo del miocardio
#> 25 10 diabetes mellitus tipo ii 2 diabetes mellitus no insulinodepeā¦
License
This package is made available under the MIT License.
Credits
This package is created and maintained by Mariana Carmona-Baez and Juan Bernardo MartĆnez Parente-CastaƱeda. š
Weāre open to suggestions, feel free to message us on mcarmonabaez@gmail.com and jbmpc@outlook.com. Pull requests are also welcome! š
Thanks
Thanks to Christopher Ormsby for his input and for letting us be part of this awesome project :hospital:
Thanks to Teresa Ortiz for her invaluable guidance :crystal_ball:
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