The R package injurymatrix purpose is to facilitate the use of the ICD-10-CM injury matrixin data analysis. The online matrices were updated in October 2020( ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/injury/tools/). Find more guidance in the use of ICD-10-CM for the analysis of injury hospitalization data in this publication: Surveillance Case Definition for Injury Hospitalizations
This injurymatrix R package provides three main functions:
matrix_intent(), matrix_mechanism() and matrix_intent_mechanism()
to add respectively intent only, mechanism only, and combination of
intent and mechanism of injury to the inputed data. The analyst has the
option to use keywords to limit the query of intent or mechanism. Try
?matrix_intent, ?matrix_mechanism and
?matrix_intent_mechanism for more information on those functions.
There are more capabilities in the package
useicd10cm to consider.
To install and load the injurymatrix package into your working environment:
- Install the devtools package:
install.packages("devtools") - Install the injurymatrix package:
devtools::install_github("epinotes/injurymatrix") - Load the package:
library(injurymatrix)
# loading relevant packages
library(tidyverse)
#> -- Attaching packages ----------------------------------------------------------- tidyverse 1.3.0 --
#> v ggplot2 3.3.2 v purrr 0.3.4
#> v tibble 3.0.1.9000 v dplyr 1.0.0
#> v tidyr 1.1.0 v stringr 1.4.0
#> v readr 1.3.1 v forcats 0.5.0
#> -- Conflicts -------------------------------------------------------------- tidyverse_conflicts() --
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
library(injurymatrix)# check the content of the dataset used in the examples below.
set.seed(11)
icd10cm_data150 %>% sample_n(10)
#> # A tibble: 10 x 6
#> uid diagnosis_1 diagnosis_2 diagnosis_3 ecode1 ecode2
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 007 T405X1A J9602 J9601 <NA> <NA>
#> 2 097 T401X1A I468 F1120 <NA> <NA>
#> 3 107 T426X1A G92 N179 Y929 <NA>
#> 4 128 T50992A J9601 J690 Y92410 <NA>
#> 5 241 T43621A I214 K7200 <NA> <NA>
#> 6 203 T40601A J9601 J189 Y92009 <NA>
#> 7 067 T43212A R570 T447X2A Y907 <NA>
#> 8 033 T40602A N390 T391X2A <NA> <NA>
#> 9 079 T433X2A F332 K760 Y92002 <NA>
#> 10 030 T447X2A J9600 G9340 <NA> <NA>
# get the indices of the columns with ICD-10_CM.
grep("diag|ecode", names(icd10cm_data150), ignore.case = T)
#> [1] 2 3 4 5 6
# The indices will be used as arguments in the following functions. - Without keyword submitted, all the five injury intents are added to the data.
- With keywords (the partial name of the intent will suffice) only the matching intents will be added to the dataset.
# ?matrix_intent for more information
# No keyword is used
results_1 <- icd10cm_data150 %>%
matrix_intent(inj_col = c(2:6))
results_1
#> # A tibble: 150 x 11
#> uid diagnosis_1 diagnosis_2 diagnosis_3 ecode1 ecode2 Assault
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 051 T82868A N186 D6859 Y832 <NA> 0
#> 2 171 T43011A G92 E860 <NA> <NA> 0
#> 3 228 T391X1A D72829 E785 Y92009 <NA> 0
#> 4 071 T383X2A T471X2A F329 <NA> <NA> 0
#> 5 026 T43591A J449 I10 Y92009 <NA> 0
#> 6 172 S72142A D62 D6832 W010X~ Y92018 0
#> 7 129 T8452XA A419 D693 Y831 <NA> 0
#> 8 197 T43621A R7881 E876 <NA> <NA> 0
#> 9 232 T50902A J9601 G92 Y9259 <NA> 0
#> 10 027 J189 G92 J9600 Y92238 <NA> 0
#> # ... with 140 more rows, and 4 more variables: `Intentional Self-harm` <dbl>,
#> # `Legal Intervention-War` <dbl>, Undetermined <dbl>, Unintentional <dbl>
# table of the injury intent from result_1
results_1 %>%
select(-diagnosis_1:-ecode2) %>%
pivot_longer(cols = -uid,
names_to = "intent",
values_to = "count") %>%
group_by(intent) %>%
summarise_at(vars(count), sum)
#> # A tibble: 5 x 2
#> intent count
#> <chr> <dbl>
#> 1 Assault 0
#> 2 Intentional Self-harm 56
#> 3 Legal Intervention-War 0
#> 4 Undetermined 4
#> 5 Unintentional 78
# Keywords used
results_2 <- icd10cm_data150 %>%
matrix_intent(inj_col = c(2:6), "unintent", "undeterm")
results_2
#> # A tibble: 150 x 8
#> uid diagnosis_1 diagnosis_2 diagnosis_3 ecode1 ecode2 Undetermined
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 051 T82868A N186 D6859 Y832 <NA> 0
#> 2 171 T43011A G92 E860 <NA> <NA> 0
#> 3 228 T391X1A D72829 E785 Y92009 <NA> 0
#> 4 071 T383X2A T471X2A F329 <NA> <NA> 0
#> 5 026 T43591A J449 I10 Y92009 <NA> 0
#> 6 172 S72142A D62 D6832 W010X~ Y92018 0
#> 7 129 T8452XA A419 D693 Y831 <NA> 0
#> 8 197 T43621A R7881 E876 <NA> <NA> 0
#> 9 232 T50902A J9601 G92 Y9259 <NA> 0
#> 10 027 J189 G92 J9600 Y92238 <NA> 0
#> # ... with 140 more rows, and 1 more variable: Unintentional <dbl>- Without keyword submitted, all the 33 injury mechanisms are added to the data.
- With keywords (the partial name of the mechanism will suffice) only the matching mechanisms will be added to the dataset.
# ?matrix_mechanism for more information
# No keyword
results_3 <- icd10cm_data150 %>%
matrix_mechanism(inj_col = c(2:6))
#> New names:
#> * Natural_Environmental_Other -> Natural_Environmental_Other...18
#> * Natural_Environmental_Other -> Natural_Environmental_Other...19
#> New names:
#> * Natural_Environmental_Other...18 -> Natural_Environmental_Other...24
#> * Natural_Environmental_Other...19 -> Natural_Environmental_Other...25
results_3
#> # A tibble: 150 x 39
#> uid diagnosis_1 diagnosis_2 diagnosis_3 ecode1 ecode2 Bites_and_Sting~
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 051 T82868A N186 D6859 Y832 <NA> 0
#> 2 171 T43011A G92 E860 <NA> <NA> 0
#> 3 228 T391X1A D72829 E785 Y92009 <NA> 0
#> 4 071 T383X2A T471X2A F329 <NA> <NA> 0
#> 5 026 T43591A J449 I10 Y92009 <NA> 0
#> 6 172 S72142A D62 D6832 W010X~ Y92018 0
#> 7 129 T8452XA A419 D693 Y831 <NA> 0
#> 8 197 T43621A R7881 E876 <NA> <NA> 0
#> 9 232 T50902A J9601 G92 Y9259 <NA> 0
#> 10 027 J189 G92 J9600 Y92238 <NA> 0
#> # ... with 140 more rows, and 32 more variables:
#> # Bites_and_Stings_venomous <dbl>, Bites_Stings_venomous <dbl>,
#> # Cut_Pierce <dbl>, Drowning_Submersion <dbl>, Fall <dbl>, Fire_Flame <dbl>,
#> # Firearm <dbl>, Hot_Object_Substance <dbl>, Machinery <dbl>,
#> # Motor_Vehicle_Nontraffic <dbl>, MVT_Motorcyclist <dbl>, MVT_Occupant <dbl>,
#> # MVT_Other <dbl>, MVT_Pedal_Cyclist <dbl>, MVT_Pedestrian <dbl>,
#> # MVT_Unspecified <dbl>, Natural_Environmental_Other...24 <dbl>,
#> # Natural_Environmental_Other...25 <dbl>, Other_Land_Transport <dbl>,
#> # Other_Specified_Child_Adult_Abuse <dbl>,
#> # Other_Specified_Classifiable <dbl>, Other_Specified_Foreign_Body <dbl>,
#> # Other_Specified_NEC <dbl>, Other_Transport <dbl>, Overexertion <dbl>,
#> # Pedal_cyclist_other <dbl>, Pedestrian_other <dbl>, Poisoning_Drug <dbl>,
#> # Poisoning_Non_drug <dbl>, Struck_by_against <dbl>, Suffocation <dbl>,
#> # Unspecified <dbl>
# Keyword used
results_4 <- icd10cm_data150 %>%
matrix_mechanism(inj_col = c(2:6), "drug", "fall", "pierce")
results_4
#> # A tibble: 150 x 10
#> uid diagnosis_1 diagnosis_2 diagnosis_3 ecode1 ecode2 Cut_Pierce Fall
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
#> 1 051 T82868A N186 D6859 Y832 <NA> 0 0
#> 2 171 T43011A G92 E860 <NA> <NA> 0 0
#> 3 228 T391X1A D72829 E785 Y92009 <NA> 0 0
#> 4 071 T383X2A T471X2A F329 <NA> <NA> 0 0
#> 5 026 T43591A J449 I10 Y92009 <NA> 0 0
#> 6 172 S72142A D62 D6832 W010X~ Y92018 0 1
#> 7 129 T8452XA A419 D693 Y831 <NA> 0 0
#> 8 197 T43621A R7881 E876 <NA> <NA> 0 0
#> 9 232 T50902A J9601 G92 Y9259 <NA> 0 0
#> 10 027 J189 G92 J9600 Y92238 <NA> 0 0
#> # ... with 140 more rows, and 2 more variables: Poisoning_Drug <dbl>,
#> # Poisoning_Non_drug <dbl>
# table of selected mechanisms from result_4
results_4 %>%
select(-diagnosis_1:-ecode2) %>%
pivot_longer(cols = -uid,
names_to = "mechanism",
values_to = "count") %>%
group_by(mechanism) %>%
summarise_at(vars(count), sum)
#> # A tibble: 4 x 2
#> mechanism count
#> <chr> <dbl>
#> 1 Cut_Pierce 2
#> 2 Fall 12
#> 3 Poisoning_Drug 118
#> 4 Poisoning_Non_drug 0- Without keyword submitted, all the 92 injury intents and mechanisms combined are added to the data.
- With keywords (the partial name of the mechanism or intent will suffice) only the matching combination of intent and mechanisms will be added to the dataset.
# ?matrix_mechanism for more information
# No keyword
results_5 <- icd10cm_data150 %>%
matrix_intent_mechanism(inj_col = c(2:6))
results_5
#> # A tibble: 150 x 98
#> uid diagnosis_1 diagnosis_2 diagnosis_3 ecode1 ecode2 Assault_Bites_S~
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 051 T82868A N186 D6859 Y832 <NA> 0
#> 2 171 T43011A G92 E860 <NA> <NA> 0
#> 3 228 T391X1A D72829 E785 Y92009 <NA> 0
#> 4 071 T383X2A T471X2A F329 <NA> <NA> 0
#> 5 026 T43591A J449 I10 Y92009 <NA> 0
#> 6 172 S72142A D62 D6832 W010X~ Y92018 0
#> 7 129 T8452XA A419 D693 Y831 <NA> 0
#> 8 197 T43621A R7881 E876 <NA> <NA> 0
#> 9 232 T50902A J9601 G92 Y9259 <NA> 0
#> 10 027 J189 G92 J9600 Y92238 <NA> 0
#> # ... with 140 more rows, and 91 more variables: Assault_Cut_Pierce <dbl>,
#> # Assault_Drowning_Submersion <dbl>, Assault_Fall <dbl>,
#> # Assault_Fire_Flame <dbl>, Assault_Firearm <dbl>,
#> # Assault_Hot_Object_Substance <dbl>, Assault_MVT_Occupant <dbl>,
#> # Assault_MVT_Pedestrian <dbl>, Assault_Natural_Environmental_Other <dbl>,
#> # Assault_Other_Land_Transport <dbl>,
#> # Assault_Other_Specified_Child_Adult_Abuse <dbl>,
#> # Assault_Other_Specified_Classifiable <dbl>,
#> # Assault_Other_Specified_NEC <dbl>, Assault_Other_Transport <dbl>,
#> # Assault_Poisoning_Drug <dbl>, Assault_Poisoning_Non_drug <dbl>,
#> # Assault_Struck_by_against <dbl>, Assault_Suffocation <dbl>,
#> # Assault_Unspecified <dbl>, `Intentional
#> # Self-harm_Bites_and_Stings_venomous` <dbl>, `Intentional
#> # Self-harm_Cut_Pierce` <dbl>, `Intentional
#> # Self-harm_Drowning_Submersion` <dbl>, `Intentional Self-harm_Fall` <dbl>,
#> # `Intentional Self-harm_Fire_Flame` <dbl>, `Intentional
#> # Self-harm_Firearm` <dbl>, `Intentional
#> # Self-harm_Hot_Object_Substance` <dbl>, `Intentional
#> # Self-harm_MVT_Occupant` <dbl>, `Intentional Self-harm_MVT_Other` <dbl>,
#> # `Intentional Self-harm_Natural_Environmental_Other` <dbl>, `Intentional
#> # Self-harm_Other_Land_Transport` <dbl>, `Intentional
#> # Self-harm_Other_Specified_Classifiable` <dbl>, `Intentional
#> # Self-harm_Other_Specified_NEC` <dbl>, `Intentional
#> # Self-harm_Other_Transport` <dbl>, `Intentional
#> # Self-harm_Poisoning_Drug` <dbl>, `Intentional
#> # Self-harm_Poisoning_Non_drug` <dbl>, `Intentional
#> # Self-harm_Struck_by_against` <dbl>, `Intentional
#> # Self-harm_Suffocation` <dbl>, `Intentional Self-harm_Unspecified` <dbl>,
#> # `Legal Intervention-War_Cut_Pierce` <dbl>, `Legal
#> # Intervention-War_Fire_Flame` <dbl>, `Legal Intervention-War_Firearm` <dbl>,
#> # `Legal Intervention-War_Other_Specified_Classifiable` <dbl>, `Legal
#> # Intervention-War_Other_Specified_NEC` <dbl>, `Legal
#> # Intervention-War_Other_Transport` <dbl>, `Legal
#> # Intervention-War_Poisoning_Non_drug` <dbl>, `Legal
#> # Intervention-War_Struck_by_against` <dbl>, `Legal
#> # Intervention-War_Suffocation` <dbl>, `Legal
#> # Intervention-War_Unspecified` <dbl>,
#> # Undetermined_Bites_and_Stings_venomous <dbl>,
#> # Undetermined_Cut_Pierce <dbl>, Undetermined_Drowning_Submersion <dbl>,
#> # Undetermined_Fall <dbl>, Undetermined_Fire_Flame <dbl>,
#> # Undetermined_Firearm <dbl>, Undetermined_Hot_Object_Substance <dbl>,
#> # Undetermined_MVT_Unspecified <dbl>,
#> # Undetermined_Natural_Environmental_Other <dbl>,
#> # Undetermined_Other_Specified_Classifiable <dbl>,
#> # Undetermined_Other_Specified_NEC <dbl>, Undetermined_Poisoning_Drug <dbl>,
#> # Undetermined_Poisoning_Non_drug <dbl>,
#> # Undetermined_Struck_by_against <dbl>, Undetermined_Suffocation <dbl>,
#> # Unintentional_Bites_and_Stings_nonvenomous <dbl>,
#> # Unintentional_Bites_and_Stings_venomous <dbl>,
#> # Unintentional_Cut_Pierce <dbl>, Unintentional_Drowning_Submersion <dbl>,
#> # Unintentional_Fall <dbl>, Unintentional_Fire_Flame <dbl>,
#> # Unintentional_Firearm <dbl>, Unintentional_Hot_Object_Substance <dbl>,
#> # Unintentional_Machinery <dbl>,
#> # Unintentional_Motor_Vehicle_Nontraffic <dbl>,
#> # Unintentional_MVT_Motorcyclist <dbl>, Unintentional_MVT_Occupant <dbl>,
#> # Unintentional_MVT_Other <dbl>, Unintentional_MVT_Pedal_Cyclist <dbl>,
#> # Unintentional_MVT_Pedestrian <dbl>,
#> # Unintentional_Natural_Environmental_Other <dbl>,
#> # Unintentional_Other_Land_Transport <dbl>,
#> # Unintentional_Other_Specified_Classifiable <dbl>,
#> # Unintentional_Other_Specified_Foreign_Body <dbl>,
#> # Unintentional_Other_Transport <dbl>, Unintentional_Overexertion <dbl>,
#> # Unintentional_Pedal_cyclist_other <dbl>,
#> # Unintentional_Pedestrian_other <dbl>, Unintentional_Poisoning_Drug <dbl>,
#> # Unintentional_Poisoning_Non_drug <dbl>,
#> # Unintentional_Struck_by_against <dbl>, Unintentional_Suffocation <dbl>,
#> # Unintentional_Unspecified <dbl>
# Keyword used
results_6 <- icd10cm_data150 %>%
matrix_intent_mechanism(inj_col = c(2:6), "Poisoning_Drug")
results_6
#> # A tibble: 150 x 10
#> uid diagnosis_1 diagnosis_2 diagnosis_3 ecode1 ecode2 Assault_Poisoni~
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 051 T82868A N186 D6859 Y832 <NA> 0
#> 2 171 T43011A G92 E860 <NA> <NA> 0
#> 3 228 T391X1A D72829 E785 Y92009 <NA> 0
#> 4 071 T383X2A T471X2A F329 <NA> <NA> 0
#> 5 026 T43591A J449 I10 Y92009 <NA> 0
#> 6 172 S72142A D62 D6832 W010X~ Y92018 0
#> 7 129 T8452XA A419 D693 Y831 <NA> 0
#> 8 197 T43621A R7881 E876 <NA> <NA> 0
#> 9 232 T50902A J9601 G92 Y9259 <NA> 0
#> 10 027 J189 G92 J9600 Y92238 <NA> 0
#> # ... with 140 more rows, and 3 more variables: `Intentional
#> # Self-harm_Poisoning_Drug` <dbl>, Undetermined_Poisoning_Drug <dbl>,
#> # Unintentional_Poisoning_Drug <dbl>
# table of selected mechanisms from result_4
results_6 %>%
select(-diagnosis_1:-ecode2) %>%
pivot_longer(cols = -uid,
names_to = "intent_mechanism",
values_to = "count") %>%
group_by(intent_mechanism) %>%
summarise_at(vars(count), sum)
#> # A tibble: 4 x 2
#> intent_mechanism count
#> <chr> <dbl>
#> 1 Assault_Poisoning_Drug 0
#> 2 Intentional Self-harm_Poisoning_Drug 56
#> 3 Undetermined_Poisoning_Drug 4
#> 4 Unintentional_Poisoning_Drug 58This example illustrates how to create a first valid external cause field.
icd10cm__external_cause_ <- "(^[VWX]\\d....|(?!(Y0[79]))Y[0-3]....|Y07.{1,3}|Y09|(T3[679]9|T414|T427|T4[3579]9)[1-4].|(?!(T3[679]9|T414|T427|T4[3579]9))(T3[6-9]|T4[0-9]|T50)..[1-4]|T1491.{0,1}|(T1[5-9]|T5[1-9]|T6[0-5]|T7[1346])...|T75[0-3]..)(A|$)"The function icd_first_valid_regex() in combination with the regular
expression above, icd10cm__external_cause_ (It is in the CSTE
Toolkit)
will create the first valid external cause field.
results_7 <- icd10cm_data150 %>%
mutate(ex_cause1 = icd_first_valid_regex(., colvec = c(2:6),
pattern = icd10cm__external_cause_))
results_7
#> # A tibble: 150 x 7
#> uid diagnosis_1 diagnosis_2 diagnosis_3 ecode1 ecode2 ex_cause1
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 051 T82868A N186 D6859 Y832 <NA> <NA>
#> 2 171 T43011A G92 E860 <NA> <NA> T43011A
#> 3 228 T391X1A D72829 E785 Y92009 <NA> T391X1A
#> 4 071 T383X2A T471X2A F329 <NA> <NA> T383X2A
#> 5 026 T43591A J449 I10 Y92009 <NA> T43591A
#> 6 172 S72142A D62 D6832 W010XXA Y92018 W010XXA
#> 7 129 T8452XA A419 D693 Y831 <NA> <NA>
#> 8 197 T43621A R7881 E876 <NA> <NA> T43621A
#> 9 232 T50902A J9601 G92 Y9259 <NA> T50902A
#> 10 027 J189 G92 J9600 Y92238 <NA> <NA>
#> # ... with 140 more rowsAdding selected intents to results_7 by using the new ex_cause1 only:
results_7 %>%
matrix_intent(inj_col = "ex_cause1",
"unintent", "undeterm")
#> # A tibble: 150 x 9
#> uid diagnosis_1 diagnosis_2 diagnosis_3 ecode1 ecode2 ex_cause1
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 051 T82868A N186 D6859 Y832 <NA> <NA>
#> 2 171 T43011A G92 E860 <NA> <NA> T43011A
#> 3 228 T391X1A D72829 E785 Y92009 <NA> T391X1A
#> 4 071 T383X2A T471X2A F329 <NA> <NA> T383X2A
#> 5 026 T43591A J449 I10 Y92009 <NA> T43591A
#> 6 172 S72142A D62 D6832 W010X~ Y92018 W010XXA
#> 7 129 T8452XA A419 D693 Y831 <NA> <NA>
#> 8 197 T43621A R7881 E876 <NA> <NA> T43621A
#> 9 232 T50902A J9601 G92 Y9259 <NA> T50902A
#> 10 027 J189 G92 J9600 Y92238 <NA> <NA>
#> # ... with 140 more rows, and 2 more variables: Undetermined <dbl>,
#> # Unintentional <dbl>Using the function matrix_valid_external() providing the columns with
the icd-10-cm of interest.
# create a new binary variable "valid_external"
results_8 <- icd10cm_data150 %>%
matrix_valid_external(c(2:6))
results_8
#> # A tibble: 150 x 7
#> uid diagnosis_1 diagnosis_2 diagnosis_3 ecode1 ecode2 valid_external
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 051 T82868A N186 D6859 Y832 <NA> 0
#> 2 171 T43011A G92 E860 <NA> <NA> 1
#> 3 228 T391X1A D72829 E785 Y92009 <NA> 1
#> 4 071 T383X2A T471X2A F329 <NA> <NA> 1
#> 5 026 T43591A J449 I10 Y92009 <NA> 1
#> 6 172 S72142A D62 D6832 W010XXA Y92018 1
#> 7 129 T8452XA A419 D693 Y831 <NA> 0
#> 8 197 T43621A R7881 E876 <NA> <NA> 1
#> 9 232 T50902A J9601 G92 Y9259 <NA> 1
#> 10 027 J189 G92 J9600 Y92238 <NA> 0
#> # ... with 140 more rows
# count the number of records with valid external cause of injury
results_8 %>%
count(valid_external)
#> # A tibble: 2 x 2
#> valid_external n
#> <dbl> <int>
#> 1 0 14
#> 2 1 136
# Subset the dataset to the 136 records with valid external causes of injury.
results_8 %>%
filter(valid_external == 1)
#> # A tibble: 136 x 7
#> uid diagnosis_1 diagnosis_2 diagnosis_3 ecode1 ecode2 valid_external
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 171 T43011A G92 E860 <NA> <NA> 1
#> 2 228 T391X1A D72829 E785 Y92009 <NA> 1
#> 3 071 T383X2A T471X2A F329 <NA> <NA> 1
#> 4 026 T43591A J449 I10 Y92009 <NA> 1
#> 5 172 S72142A D62 D6832 W010XXA Y92018 1
#> 6 197 T43621A R7881 E876 <NA> <NA> 1
#> 7 232 T50902A J9601 G92 Y9259 <NA> 1
#> 8 066 T43012A J9692 J690 Y92019 W1839XA 1
#> 9 118 S32810A S06340A I2699 V0319XA <NA> 1
#> 10 076 T43622A J9600 T40992A <NA> <NA> 1
#> # ... with 126 more rowsExploring the datasets below that provided the necessary information used by the functions described above. Run the following lines of code to get more details on the datasets.
library(injurymatix)
?icd10cm_mech_regex # matrix collapsed to the 33 mechanisms
?icd10cm_intent_regex # matrix collapsed to the 5 intents
?icd10cm_intent_mech_regex # matrix of the 92 combinations of intent
and mechanism ?injury_matrix_all # the full matrix of 3,655 entries
ICD-10-CM of external causes of injury