Database Credentialed Access
MIMIC-IV
Alistair Johnson , Lucas Bulgarelli , Tom Pollard , Steven Horng , Leo Anthony Celi , Roger Mark
Published: Aug. 13, 2020. Version: 0.4 <View latest version>
Guidelines for creating datasets and models from MIMIC (April 24, 2024, 10:12 a.m.)
We recognize that there is value in creating datasets or models that are either derived from MIMIC or which augment MIMIC in some way (for example, by adding annotations). Here are some guidelines on creating these datasets and models:
- Any derived datasets or models should be treated as containing sensitive information. If you wish to share these resources, they should be shared on PhysioNet under the same agreement as the source data.
- If you would like to use the MIMIC acronym in your project name, please include the letters “Ext” (for example, MIMIC-IV-Ext-YOUR-DATASET"). Ext may either indicate “extracted” (e.g. a derived subset) or “extended” (e.g. annotations), depending on your use case.
When using this resource, please cite:
(show more options)
Johnson, A., Bulgarelli, L., Pollard, T., Horng, S., Celi, L. A., & Mark, R. (2020). MIMIC-IV (version 0.4). PhysioNet. https://doi.org/10.13026/a3wn-hq05.
Please include the standard citation for PhysioNet:
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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.
Abstract
Retrospectively collected medical data has the opportunity to improve patient care through knowledge discovery and algorithm development. Broad reuse of medical data is desirable for the greatest public good, but data sharing must be done in a manner which protects patient privacy. The Medical Information Mart for Intensive Care (MIMIC)-III database provided critical care data for over 40,000 patients admitted to intensive care units at the Beth Israel Deaconess Medical Center (BIDMC). Importantly, MIMIC-III was deidentified, and patient identifiers were removed according to the Health Insurance Portability and Accountability Act (HIPAA) Safe Harbor provision. MIMIC-III has been integral in driving large amounts of research in clinical informatics, epidemiology, and machine learning. Here we present MIMIC-IV, an update to MIMIC-III, which incorporates contemporary data and improves on numerous aspects of MIMIC-III. MIMIC-IV adopts a modular approach to data organization, highlighting data provenance and facilitating both individual and combined use of disparate data sources. MIMIC-IV is intended to carry on the success of MIMIC-III and support a broad set of applications within healthcare.
Background
In recent years there has been a concerted move towards the adoption of digital health record systems in hospitals. In the US, nearly 96% of hospitals had a digital electronic health record (EHR) in 2015 [1]. Retrospectively collected medical data has increasingly been used for epidemiology and predictive modeling. The latter is in part due to the effectiveness of modeling approaches on large datasets [2].
Despite these advances, access to medical data to improve patient care remains a significant challenge. While the reasons for limited sharing of medical data are multifaceted, concerns around patient privacy are highlighted as one of the most significant issues. Although patient studies have shown almost uniform agreement that deidentified medical data should be used to improve medical practice, domain experts continue to debate the optimal mechanisms of doing so. Uniquely, the MIMIC-III database adopted a permissive access scheme which allowed for broad reuse of the data [3]. This mechanism has been successful in the wide use of MIMIC-III in a variety of studies ranging from assessment of treatment efficacy in well defined cohorts to prediction of key patient outcomes such as mortality. MIMIC-IV aims to carry on the success of MIMIC-III, with a number of changes to improve usability of the data and enable more research applications.
Methods
MIMIC-IV is sourced from two in-hospital database systems: a custom hospital wide EHR and an ICU specific clinical information system. The creation of MIMIC-IV was carried out in three steps:
- Acquisition. Data for patients who were admitted to the BIDMC emergency department or one of the intensive care units were extracted from the respective hospital databases. A master patient list was created which contained all medical record numbers corresponding to patients admitted to an ICU or the emergency department between 2008 - 2019. All source tables were filtered to only rows related to patients in the master patient list.
- Preparation. The data were reorganized to better facilitate retrospective data analysis. This included the denormalization of tables, removal of audit trails, and reorganization into fewer tables. The aim of this process is to simplify retrospective analysis of the database. Importantly, data cleaning steps were not performed, to ensure the data reflects a real-world clinical dataset.
- Deidentify. Patient identifiers as stipulated by HIPAA were removed. Patient identifiers were replaced using a random cipher, resulting in deidentified integer identifiers for patients, hospitalizations, and ICU stays. Structured data were filtered using look up tables and allow lists. If necessary, a free-text deidentification algorithm was applied to remove PHI from free-text. Finally, date and times were shifted randomly into the future using an offset measured in days. A single date shift was assigned to each subject_id. As a result, the data for a single patient are internally consistent. For example, if the time between two measures in the database was 4 hours in the raw data, then the calculated time difference in MIMIC-IV will also be 4 hours. Conversely, distinct patients are not temporally comparable. That is, two patients admitted in 2130 were not necessarily admitted in the same year.
After these three steps were carried out, the database was exported to a character based comma delimited format.
Data Description
MIMIC-IV is grouped into three modules: core, hosp, and icu. The aim of these modules is to highlight their intended use and provenance. Up to date documentation for MIMIC-IV is available on the MIMIC-IV website.
core
The core module stores patient tracking information necessary for any data analysis using MIMIC-IV. The core module contains three tables: patients, admissions, and transfers. These tables provide demographics for the patient, a record for each hospitalization, and a record for each ward stay within a hospitalization.
Notably, the patients table provides timing information for each patient through the anchor_year and anchor_year_group columns. The anchor_year is a deidentified year occurring sometime between 2100 - 2200, and the anchor_year_group is a three year long date ranges between 2008 - 2019. These pieces of information allow researchers to infer the approximate year a patient received care. For example, if a patient's anchor_year is 2158, and their anchor_year_group is 2011 - 2013, then any hospitalizations for the patient occurring in the year 2158 actually occurred sometime between 2011 - 2013. Finally, the anchor_age provides the patient age in the given anchor_year. If the patient was over 89 in the anchor_year, this anchor_age has been set to 91 (i.e. all patients over 89 have been grouped together into a single group with value 91, regardless of what their real age was).
hosp
The hosp module contains data derived from the hospital wide EHR. These measurements are predominantly recorded during the hospital stay, though some tables include data from outside the hospital as well (e.g. outpatient laboratory tests in labevents). Information includes laboratory measurements (labevents, d_labitems), microbiology cultures (microbiologyevents, d_micro), provider orders (poe, poe_detail), medication administration (emar, emar_detail), medication prescription (prescriptions, pharmacy), hospital billing information (diagnoses_icd, d_icd_diagnoses, procedures_icd, d_icd_procedures, hcpcsevents, d_hcpcs, drgcodes), and service related information (services).
icu
The icu module contains data sourced from the clinical information system at the BIDMC: MetaVision (iMDSoft). MetaVision tables were denormalized to create a star schema where the icustays and d_items tables link to a set of data tables all suffixed with "events". Data documented in the icu module includes intravenous and fluid inputs (inputevents), patient outputs (outputevents), procedures (procedureevents), information documented as a date or time (datetimeevents), and other charted information (chartevents). All events tables contain a stay_id column allowing identification of the associated ICU patient in icustays, and an itemid column allowing identification of the concept documented in d_items.
Usage Notes
The data described here are collected during routine clinical practice and reflect the idiosyncrasies of that practice. Implausible values may be present in the database as an artifact of the archival process. Researchers should follow best practice guidelines when analyzing the data.
We have created an open source repository for the sharing of code and discussion of the database, referred to as the MIMIC-IV Code Repository. The code repository provides a mechanism for shared discussion and analysis of MIMIC-IV.
Release Notes
Current version
The current version of MIMIC-IV is v0.4. As the database is still in development, we may change the schema in future versions. Our aim is to eventually release MIMIC-IV v1.0, at which point schema changes will respect the semantic versioning.
v0.4
- d_micro
- This table has been removed
- microbiologyevents
- Added the column spec_type_desc, test_name, org_name, and ab_name columns
- These columns contain the textual name of the organism/antibiotic/test/specimen
- Added the comments column: this column contains information about the test, and in some cases (e.g. viral load tests), contains the result
v0.3
- Fixed a bug in the timing between hosp and icu
v0.2
- Updated demographics in the patient table
anchor_year
->anchor_year_group
anchor_year_shifted
->anchor_year
- See the patients table for detail on these columns
- transfers
- Deleted the
los
column
- Deleted the
- emar
mar_id
->emar_id
emar_id
is now a composite ofsubject_id
andemar_seq
, and has form “subject_id-emar_seq”emar_seq
column - a monotonically increasing integer starting with the first eMAR administration- Added
poe_id
andpharmacy_id
columns for linking to those tables
- emar_detail
mar_id
->emar_id
(changed as above)- Deleted the
mar_detail_id
column
- hcpcsevents
ticket_id_seq
->seq_num
- labevents
- Many previously NULL values are now populated - these were removed originally due to deidentification
- Added the
comments
column. This contains deidentified free-text comments with labs. PHI is replaced with three underscores (___
). If an entire comment is___
, then the entire comment was scrubbed.
- microbiologyevents
stay_id
column removedspec_id
->micro_specimen_id
- Added the poe and poe_detail tables
- Documentation of provider orders for various treatments and other aspects of patient management
- Added the prescriptions table
- Documentation of medicine prescriptions via the provider order interface
- Added the pharmacy table
- Detailed information regarding prescriptions provided by the pharmacy including formulary dose, route, frequency, dose, and so on.
- inputevents
- Fixed an error in the calculation of the amount column
- icustays
- Re-derived
stay_id
- the newstay_id
are distinct from the previous version.
- Re-derived
Acknowledgements
We would like to thank the Beth Israel Deaconess Medical Center for their continued support of the MIMIC project. In particular we would like to thank Carolyn Conti, Alvin Gayles, Larry Markson, Ayad Shammout, Lu Shen, and Manu Tandon for their assistance. This work was supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under NIH grant number R01EB030362.Conflicts of Interest
None to declare.
References
- Henry, J., Pylypchuk, Y., Searcy T. & Patel V. (May 2016). Adoption of Electronic Health Record Systems among U.S. Non-Federal Acute Care Hospitals: 2008-2015. ONC Data Brief, no.35. Office of the National Coordinator for Health Information Technology: Washington DC.+
- Halevy, A., Norvig, P., & Pereira, F. (2009). The unreasonable effectiveness of data. IEEE Intelligent Systems, 24(2), 8-12.
- Johnson, A. E., Pollard, T. J., Shen, L., Lehman, L.H., Feng, M., Ghassemi, M., ... & Mark, R. G. (2016). MIMIC-III, a freely accessible critical care database. Scientific data, 3(1), 1-9.
Access
Access Policy:
Only credentialed users who sign the DUA can access the files.
License (for files):
PhysioNet Credentialed Health Data License 1.5.0
Data Use Agreement:
PhysioNet Credentialed Health Data Use Agreement 1.5.0
Required training:
CITI Data or Specimens Only Research
Discovery
DOI (version 0.4):
https://doi.org/10.13026/a3wn-hq05
DOI (latest version):
https://doi.org/10.13026/07hj-2a80
Topics:
critical care
intensive care unit
machine learning
mimic
Project Website:
https://mimic-iv.mit.edu
Corresponding Author
Files
- be a credentialed user
- complete required training:
- CITI Data or Specimens Only Research You may submit your training here.
- sign the data use agreement for the project