README.md
In hdshea/MIMIC4db: Package for building, loading and referencing the Google BigQuery verion of the MIMIC-IV
MIMIC4db
This work references data from the Medical Information Mart for
Intensive Care MIMIC-IV database. MIMIC-IV is a large, freely-available
database comprising de-identified health-related data from patients who
were admitted to the critical care units of the Beth Israel Deaconess
Medical Center from 2008-2019. Detailed information can be obtained on
the MIMIC-IV website.
MIMIC4db provides a tightly bound set of routines to reference the
Google BigQuery version of the MIMIC-IV
v1.0 database using base
access routines from the DBI R package
with an appropriate bigrquery
DBIConnection.
Installation
You can install MIMIC4db from GitHub with:
# install.packages("devtools")
devtools::install_github("hdshea/MIMIC4db")
Basic Usage
These are basic examples which show you how to solve a common problem.
Connecting to the database:
library(MIMIC4db)
# To connect to the database, you must provide an appropriate project (in the `your-project`
# slot below) which has billing set up and to which you have write access. This project
# must be approved to access the PhysioNet BigQuery MIMIC schemas.
con <- bigrquery::dbConnect(
bigrquery::bigquery(),
project = "your-project",
quiet = TRUE
)
Using the meta data functions:
core <- m4_meta_data()
core <- core[core$module == "mimic_core", ]
for(i in 1:length(core)) {
cat(stringr::str_c("select * from physionet-data.",
core[i,"module"], ".", core[i,"table"], "\n"))
}
#> select * from physionet-data.mimic_core.admissions
#> select * from physionet-data.mimic_core.patients
#> select * from physionet-data.mimic_core.transfers
neuro <- m4_service_decsriptions()
neuro <- neuro[startsWith(neuro$short_description, "Neurologic"), ]
neuro[,c("service", "short_description")]
#> # A tibble: 2 × 2
#> service short_description
#> <chr> <chr>
#> 1 NMED Neurologic Medical
#> 2 NSURG Neurologic Surgical
Using the two base functions:
d_items <- m4_select_data(
con,
"select * from physionet-data.mimic_icu.d_items order by itemid limit 5"
)
d_items
#> # A tibble: 5 × 9
#> itemid label abbreviation linksto category unitname param_type lownormalvalue
#> <int> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 220003 ICU A… ICU Admissi… dateti… ADT <NA> Date and … NA
#> 2 220045 Heart… HR charte… Routine… bpm Numeric NA
#> 3 220046 Heart… HR Alarm - … charte… Alarms bpm Numeric NA
#> 4 220047 Heart… HR Alarm - … charte… Alarms bpm Numeric NA
#> 5 220048 Heart… Heart Rhythm charte… Routine… <NA> Text NA
#> # … with 1 more variable: highnormalvalue <dbl>
d_items <- m4_get_from_table(con, "d_items", where = "where itemid <= 220048")
d_items
#> # A tibble: 5 × 9
#> itemid label abbreviation linksto category unitname param_type lownormalvalue
#> <int> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 220003 ICU A… ICU Admissi… dateti… ADT <NA> Date and … NA
#> 2 220046 Heart… HR Alarm - … charte… Alarms bpm Numeric NA
#> 3 220047 Heart… HR Alarm - … charte… Alarms bpm Numeric NA
#> 4 220048 Heart… Heart Rhythm charte… Routine… <NA> Text NA
#> 5 220045 Heart… HR charte… Routine… bpm Numeric NA
#> # … with 1 more variable: highnormalvalue <dbl>
Using the tables specific data access functions:
tab <- m4_chartevents_items(con)
tab[c(1:5), ]
#> # A tibble: 5 × 8
#> itemid label abbreviation category unitname param_type lownormalvalue
#> <int> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 224188 PICC Lin… PICC Line Site… Access Li… <NA> Text NA
#> 2 224281 Multi Lu… Multi Lumen Si… Access Li… <NA> Text NA
#> 3 224283 Multi Lu… Multi Lumen Wa… Access Li… <NA> Text NA
#> 4 224289 Arterial… Arterial line … Access Li… <NA> Text NA
#> 5 224291 Arterial… Arterial line … Access Li… <NA> Text NA
#> # … with 1 more variable: highnormalvalue <dbl>
pat <- m4_patients(con, cohort = 16904137)
pat
#> # A tibble: 1 × 6
#> subject_id gender anchor_age anchor_year anchor_year_group dod
#> <int> <chr> <int> <int> <chr> <date>
#> 1 16904137 M 54 2117 2017 - 2019 NA
svs <- m4_services(con, cohort = 16904137)
svs
#> # A tibble: 2 × 11
#> subject_id hadm_id transfertime service_seq first_service curr_service
#> <int> <int> <dttm> <int> <lgl> <chr>
#> 1 16904137 21081215 2105-10-04 17:27:12 1 TRUE SURG
#> 2 16904137 22747535 2117-01-24 06:35:19 1 TRUE SURG
#> # … with 5 more variables: curr_short_desc <chr>, curr_description <chr>,
#> # prev_service <chr>, prev_short_desc <chr>, prev_description <chr>
evt <- m4_procedureevents(con, cohort = 16904137)
evt[c(1:5), ]
#> # A tibble: 5 × 26
#> subject_id hadm_id stay_id starttime endtime
#> <int> <int> <int> <dttm> <dttm>
#> 1 16904137 22747535 34698721 2117-01-24 12:50:00 2117-01-26 17:51:00
#> 2 16904137 22747535 34698721 2117-01-24 12:50:00 2117-01-26 17:51:00
#> 3 16904137 22747535 34698721 2117-01-24 12:51:00 2117-01-26 17:51:00
#> 4 16904137 22747535 34698721 2117-01-24 12:51:00 2117-01-25 04:11:00
#> 5 16904137 22747535 34698721 2117-01-24 12:52:00 2117-01-26 17:51:00
#> # … with 21 more variables: storetime <dttm>, itemid <int>, value <dbl>,
#> # valueuom <chr>, location <chr>, locationcategory <chr>, orderid <int>,
#> # linkorderid <int>, ordercategoryname <chr>,
#> # secondaryordercategoryname <chr>, ordercategorydescription <chr>,
#> # patientweight <dbl>, totalamount <dbl>, totalamountuom <chr>,
#> # isopenbag <int>, continueinnextdept <int>, cancelreason <int>,
#> # statusdescription <chr>, comments_date <dttm>, ORIGINALAMOUNT <dbl>, …
Using the common pattern data access functions:
# Combined and preprocessed patient and admission data
patadm <- m4_patient_admissions(con, cohort = 16904137)
patadm[, c(1:6)]
#> # A tibble: 2 × 6
#> subject_id hadm_id gender dod admission_seq first_admission
#> <int> <int> <chr> <date> <int> <lgl>
#> 1 16904137 21081215 M NA 1 TRUE
#> 2 16904137 22747535 M NA 2 FALSE
patadm[, c(7:10)]
#> # A tibble: 2 × 4
#> admittime dischtime los_hospital admission_age
#> <dttm> <dttm> <dbl> <dbl>
#> 1 2105-10-04 17:26:00 2105-10-12 11:11:00 7.74 42.8
#> 2 2117-01-24 06:33:00 2117-01-29 15:59:00 5.39 54.1
patadm[, c(11:14)]
#> # A tibble: 2 × 4
#> admission_decade anchor_year_group admission_type admission_location
#> <fct> <chr> <fct> <fct>
#> 1 [40,50) 2017 - 2019 URGENT TRANSFER FROM HOSPITAL
#> 2 [50,60) 2017 - 2019 EW EMER. TRANSFER FROM HOSPITAL
patadm[, c(15:19)]
#> # A tibble: 2 × 5
#> discharge_location insurance ethnicity_group language marital_status
#> <fct> <fct> <fct> <fct> <fct>
#> 1 HOME Other OTHER ENGLISH MARRIED
#> 2 HOME HEALTH CARE Other WHITE ENGLISH DIVORCED
# Combined and preprocessed patient, admission and icustay data
icudet <- m4_patient_icustays(con, cohort = 16904137)
icudet[, c(1:6)]
#> # A tibble: 2 × 6
#> subject_id hadm_id stay_id gender dod admission_seq
#> <int> <int> <int> <chr> <date> <int>
#> 1 16904137 21081215 NA M NA 1
#> 2 16904137 22747535 34698721 M NA 2
icudet[, c(7:10)]
#> # A tibble: 2 × 4
#> first_admission admittime dischtime los_hospital
#> <lgl> <dttm> <dttm> <dbl>
#> 1 TRUE 2105-10-04 17:26:00 2105-10-12 11:11:00 7.74
#> 2 FALSE 2117-01-24 06:33:00 2117-01-29 15:59:00 5.39
icudet[, c(11:14)]
#> # A tibble: 2 × 4
#> admission_age admission_decade anchor_year_group admission_type
#> <dbl> <fct> <chr> <fct>
#> 1 42.8 [40,50) 2017 - 2019 URGENT
#> 2 54.1 [50,60) 2017 - 2019 EW EMER.
icudet[, c(15:19)]
#> # A tibble: 2 × 5
#> admission_location discharge_location insurance ethnicity_group language
#> <fct> <fct> <fct> <fct> <fct>
#> 1 TRANSFER FROM HOSPITAL HOME Other OTHER ENGLISH
#> 2 TRANSFER FROM HOSPITAL HOME HEALTH CARE Other WHITE ENGLISH
icudet[, c(20:22)]
#> # A tibble: 2 × 3
#> marital_status icustay_seq first_icustay
#> <fct> <int> <lgl>
#> 1 MARRIED 1 TRUE
#> 2 DIVORCED 1 TRUE
icudet[, c(23:25)]
#> # A tibble: 2 × 3
#> intime outtime los_icustay
#> <dttm> <dttm> <dbl>
#> 1 NA NA NA
#> 2 2117-01-24 06:35:19 2117-01-26 17:51:20 2.47
Disconnecting from the database:
bigrquery::dbDisconnect(con)
Citations for MIMIC-IV publications:
Johnson, A., Bulgarelli, L., Pollard, T., Horng, S., Celi, L. A., &
Mark, R. (2021). MIMIC-IV (version 1.0). PhysioNet.
https://doi.org/10.13026/s6n6-xd98.
PhysioNet: Goldberger, A., Amaral, L., Glass, L., Hausdorff, J.,
Ivanov, P. C., Mark, R., … & Stanley, H. E. (2000). PhysioBank,
PhysioToolkit, and PhysioNet: Components of a new research resource for
complex physiologic signals. Circulation [Online]. 101 (23),
pp. e215–e220.
hdshea/MIMIC4db documentation built on Dec. 20, 2021, 3:45 p.m.
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MIMIC4db
This work references data from the Medical Information Mart for Intensive Care MIMIC-IV database. MIMIC-IV is a large, freely-available database comprising de-identified health-related data from patients who were admitted to the critical care units of the Beth Israel Deaconess Medical Center from 2008-2019. Detailed information can be obtained on the MIMIC-IV website.
MIMIC4db provides a tightly bound set of routines to reference the Google BigQuery version of the MIMIC-IV v1.0 database using base access routines from the DBI R package with an appropriate bigrquery DBIConnection.
Installation
You can install MIMIC4db from GitHub with:
# install.packages("devtools")
devtools::install_github("hdshea/MIMIC4db")
Basic Usage
These are basic examples which show you how to solve a common problem.
Connecting to the database:
library(MIMIC4db)
# To connect to the database, you must provide an appropriate project (in the `your-project`
# slot below) which has billing set up and to which you have write access. This project
# must be approved to access the PhysioNet BigQuery MIMIC schemas.
con <- bigrquery::dbConnect(
bigrquery::bigquery(),
project = "your-project",
quiet = TRUE
)
Using the meta data functions:
core <- m4_meta_data()
core <- core[core$module == "mimic_core", ]
for(i in 1:length(core)) {
cat(stringr::str_c("select * from physionet-data.",
core[i,"module"], ".", core[i,"table"], "\n"))
}
#> select * from physionet-data.mimic_core.admissions
#> select * from physionet-data.mimic_core.patients
#> select * from physionet-data.mimic_core.transfers
neuro <- m4_service_decsriptions()
neuro <- neuro[startsWith(neuro$short_description, "Neurologic"), ]
neuro[,c("service", "short_description")]
#> # A tibble: 2 × 2
#> service short_description
#> <chr> <chr>
#> 1 NMED Neurologic Medical
#> 2 NSURG Neurologic Surgical
Using the two base functions:
d_items <- m4_select_data(
con,
"select * from physionet-data.mimic_icu.d_items order by itemid limit 5"
)
d_items
#> # A tibble: 5 × 9
#> itemid label abbreviation linksto category unitname param_type lownormalvalue
#> <int> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 220003 ICU A… ICU Admissi… dateti… ADT <NA> Date and … NA
#> 2 220045 Heart… HR charte… Routine… bpm Numeric NA
#> 3 220046 Heart… HR Alarm - … charte… Alarms bpm Numeric NA
#> 4 220047 Heart… HR Alarm - … charte… Alarms bpm Numeric NA
#> 5 220048 Heart… Heart Rhythm charte… Routine… <NA> Text NA
#> # … with 1 more variable: highnormalvalue <dbl>
d_items <- m4_get_from_table(con, "d_items", where = "where itemid <= 220048")
d_items
#> # A tibble: 5 × 9
#> itemid label abbreviation linksto category unitname param_type lownormalvalue
#> <int> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 220003 ICU A… ICU Admissi… dateti… ADT <NA> Date and … NA
#> 2 220046 Heart… HR Alarm - … charte… Alarms bpm Numeric NA
#> 3 220047 Heart… HR Alarm - … charte… Alarms bpm Numeric NA
#> 4 220048 Heart… Heart Rhythm charte… Routine… <NA> Text NA
#> 5 220045 Heart… HR charte… Routine… bpm Numeric NA
#> # … with 1 more variable: highnormalvalue <dbl>
Using the tables specific data access functions:
tab <- m4_chartevents_items(con)
tab[c(1:5), ]
#> # A tibble: 5 × 8
#> itemid label abbreviation category unitname param_type lownormalvalue
#> <int> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 224188 PICC Lin… PICC Line Site… Access Li… <NA> Text NA
#> 2 224281 Multi Lu… Multi Lumen Si… Access Li… <NA> Text NA
#> 3 224283 Multi Lu… Multi Lumen Wa… Access Li… <NA> Text NA
#> 4 224289 Arterial… Arterial line … Access Li… <NA> Text NA
#> 5 224291 Arterial… Arterial line … Access Li… <NA> Text NA
#> # … with 1 more variable: highnormalvalue <dbl>
pat <- m4_patients(con, cohort = 16904137)
pat
#> # A tibble: 1 × 6
#> subject_id gender anchor_age anchor_year anchor_year_group dod
#> <int> <chr> <int> <int> <chr> <date>
#> 1 16904137 M 54 2117 2017 - 2019 NA
svs <- m4_services(con, cohort = 16904137)
svs
#> # A tibble: 2 × 11
#> subject_id hadm_id transfertime service_seq first_service curr_service
#> <int> <int> <dttm> <int> <lgl> <chr>
#> 1 16904137 21081215 2105-10-04 17:27:12 1 TRUE SURG
#> 2 16904137 22747535 2117-01-24 06:35:19 1 TRUE SURG
#> # … with 5 more variables: curr_short_desc <chr>, curr_description <chr>,
#> # prev_service <chr>, prev_short_desc <chr>, prev_description <chr>
evt <- m4_procedureevents(con, cohort = 16904137)
evt[c(1:5), ]
#> # A tibble: 5 × 26
#> subject_id hadm_id stay_id starttime endtime
#> <int> <int> <int> <dttm> <dttm>
#> 1 16904137 22747535 34698721 2117-01-24 12:50:00 2117-01-26 17:51:00
#> 2 16904137 22747535 34698721 2117-01-24 12:50:00 2117-01-26 17:51:00
#> 3 16904137 22747535 34698721 2117-01-24 12:51:00 2117-01-26 17:51:00
#> 4 16904137 22747535 34698721 2117-01-24 12:51:00 2117-01-25 04:11:00
#> 5 16904137 22747535 34698721 2117-01-24 12:52:00 2117-01-26 17:51:00
#> # … with 21 more variables: storetime <dttm>, itemid <int>, value <dbl>,
#> # valueuom <chr>, location <chr>, locationcategory <chr>, orderid <int>,
#> # linkorderid <int>, ordercategoryname <chr>,
#> # secondaryordercategoryname <chr>, ordercategorydescription <chr>,
#> # patientweight <dbl>, totalamount <dbl>, totalamountuom <chr>,
#> # isopenbag <int>, continueinnextdept <int>, cancelreason <int>,
#> # statusdescription <chr>, comments_date <dttm>, ORIGINALAMOUNT <dbl>, …
Using the common pattern data access functions:
# Combined and preprocessed patient and admission data
patadm <- m4_patient_admissions(con, cohort = 16904137)
patadm[, c(1:6)]
#> # A tibble: 2 × 6
#> subject_id hadm_id gender dod admission_seq first_admission
#> <int> <int> <chr> <date> <int> <lgl>
#> 1 16904137 21081215 M NA 1 TRUE
#> 2 16904137 22747535 M NA 2 FALSE
patadm[, c(7:10)]
#> # A tibble: 2 × 4
#> admittime dischtime los_hospital admission_age
#> <dttm> <dttm> <dbl> <dbl>
#> 1 2105-10-04 17:26:00 2105-10-12 11:11:00 7.74 42.8
#> 2 2117-01-24 06:33:00 2117-01-29 15:59:00 5.39 54.1
patadm[, c(11:14)]
#> # A tibble: 2 × 4
#> admission_decade anchor_year_group admission_type admission_location
#> <fct> <chr> <fct> <fct>
#> 1 [40,50) 2017 - 2019 URGENT TRANSFER FROM HOSPITAL
#> 2 [50,60) 2017 - 2019 EW EMER. TRANSFER FROM HOSPITAL
patadm[, c(15:19)]
#> # A tibble: 2 × 5
#> discharge_location insurance ethnicity_group language marital_status
#> <fct> <fct> <fct> <fct> <fct>
#> 1 HOME Other OTHER ENGLISH MARRIED
#> 2 HOME HEALTH CARE Other WHITE ENGLISH DIVORCED
# Combined and preprocessed patient, admission and icustay data
icudet <- m4_patient_icustays(con, cohort = 16904137)
icudet[, c(1:6)]
#> # A tibble: 2 × 6
#> subject_id hadm_id stay_id gender dod admission_seq
#> <int> <int> <int> <chr> <date> <int>
#> 1 16904137 21081215 NA M NA 1
#> 2 16904137 22747535 34698721 M NA 2
icudet[, c(7:10)]
#> # A tibble: 2 × 4
#> first_admission admittime dischtime los_hospital
#> <lgl> <dttm> <dttm> <dbl>
#> 1 TRUE 2105-10-04 17:26:00 2105-10-12 11:11:00 7.74
#> 2 FALSE 2117-01-24 06:33:00 2117-01-29 15:59:00 5.39
icudet[, c(11:14)]
#> # A tibble: 2 × 4
#> admission_age admission_decade anchor_year_group admission_type
#> <dbl> <fct> <chr> <fct>
#> 1 42.8 [40,50) 2017 - 2019 URGENT
#> 2 54.1 [50,60) 2017 - 2019 EW EMER.
icudet[, c(15:19)]
#> # A tibble: 2 × 5
#> admission_location discharge_location insurance ethnicity_group language
#> <fct> <fct> <fct> <fct> <fct>
#> 1 TRANSFER FROM HOSPITAL HOME Other OTHER ENGLISH
#> 2 TRANSFER FROM HOSPITAL HOME HEALTH CARE Other WHITE ENGLISH
icudet[, c(20:22)]
#> # A tibble: 2 × 3
#> marital_status icustay_seq first_icustay
#> <fct> <int> <lgl>
#> 1 MARRIED 1 TRUE
#> 2 DIVORCED 1 TRUE
icudet[, c(23:25)]
#> # A tibble: 2 × 3
#> intime outtime los_icustay
#> <dttm> <dttm> <dbl>
#> 1 NA NA NA
#> 2 2117-01-24 06:35:19 2117-01-26 17:51:20 2.47
Disconnecting from the database:
bigrquery::dbDisconnect(con)
Citations for MIMIC-IV publications:
Johnson, A., Bulgarelli, L., Pollard, T., Horng, S., Celi, L. A., & Mark, R. (2021). MIMIC-IV (version 1.0). PhysioNet. https://doi.org/10.13026/s6n6-xd98.
PhysioNet: Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., … & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.
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