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MIMIC-II Clinical Database
Mohammed Saeed , Mauricio Villarroel , Andrew Reisner , Gari Clifford , Li-wei Lehman , George Moody , Thomas Heldt , Tin Kyaw , Benjamin Moody , Roger Mark
Published: April 24, 2011. Version: 2.6.0
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Saeed, M., Villarroel, M., Reisner, A., Clifford, G., Lehman, L., Moody, G., Heldt, T., Kyaw, T., Moody, B., & Mark, R. (2011). MIMIC-II Clinical Database (version 2.6.0). PhysioNet. https://doi.org/10.13026/fxn0-mk84.
Saeed, Mohammed MD, PhD; Villarroel, Mauricio MBA; Reisner, Andrew T. MD; Clifford, Gari PhD; Lehman, Li-Wei PhD; Moody, George; Heldt, Thomas PhD; Kyaw, Tin H. MEng; Moody, Benjamin; Mark, Roger G. MD, PhD. Multiparameter Intelligent Monitoring in Intensive Care II: A public-access intensive care unit database*. Critical Care Medicine 39(5):p 952-960, May 2011. DOI: https://doi.org/10.1097/CCM.0b013e31820a92c6
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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.
MIMIC-II documents a diverse and large population of intensive care unit patient stays and contains comprehensive and detailed clinical data, including physiological waveforms and minute-by-minute trends for a subset of records. It establishes a unique public-access resource for critical care research, supporting a diverse range of analytic studies spanning epidemiology, clinical decision-rule development, and electronic tool development. The MIMIC-II Clinical Database, although de-identified, still contains detailed information regarding the clinical care of patients, and must be treated with appropriate care and respect.
In 2003, under National Institutes of Health funding, we established a research program with the objective of developing and evaluating advanced ICU monitoring and decision support systems. A critical requirement of our program was the development of a substantial and comprehensive clinical database from ICU patients . Now, 7 yrs later, the MIMIC-II database has reached a state of maturity sufficient to be made available to the wider research community.
The database is intended to support a wide diversity of research in critical care. Unlike related databases, there are no access fees or extensive credentialing requirements, and documentation and other support are available so that the data will be accessible to the largest community of researchers. Our work builds on the earlier efforts of Moody and Mark to create MIMIC, a database for development and evaluation of intelligent intensive care monitoring .
This first release of the MIMIC-II database encompasses adult patients admitted to ICUs at Boston’s Beth Israel Deaconess Medical Center during the period 2001–2007. The Medical Center is a 620-bed tertiary academic medical center in Boston and a level I trauma center with 77 critical care beds. The ICUs include the medical, surgical, coronary, and cardiac surgery recovery care units.ICU stays separated by >24 hrs were counted separately even if they occurred within the same hospital stay.
The data acquisition process was not visible to staff and did not interfere with the clinical care of patients or methods of monitoring. Clinical data were obtained from the CareVue Clinical Information System (models M2331A and M1215A; Philips Health-care, Andover, MA) deployed in all the study ICUs as well as from hospital electronic archives.
The data includes detailed information about intensive care unit patient stays, including laboratory data, therapeutic intervention profiles such as vasoactive medication drip rates and ventilator settings, nursing progress notes, discharge summaries, radiology reports, provider order entry data, International Classification of Diseases, 9th Revision codes.
All data was de-identified in compliance with Health Insurance Portability and Accountability Act standards to facilitate public access. Deletion of protected health information from structured data sources was generally straightforward (e.g., database fields that provide the patient name). For de-identification of free text, we developed an automated algorithm that is available for reuse [3,4].
The data is provided as a collection of CSV files that can be loaded into a relational database system. The following list provides an overview of the data categories available in MIMIC-II.
- General: Patient demographics, hospital admissions & discharge dates, room tracking, death dates (in or out of the hospital), ICD-9 codes, unique code for health care provider and type (RN, MD, RT, etc). All dates are surrogate dates due to privacy issues but time intervals (even those between multiple admissions of the same patient) are preserved.
- Physiological: Hourly vital sign metrics, SAPS, SOFA, ventilator settings, etc.
- Medications: IV meds, provider order entry data, etc.
- Lab Tests: Chemistry, hematology, ABGs, imaging, etc.
- Fluid Balance: Intake (solutions, blood, etc) and output (urine, estimated blood loss, etc).
- Notes & Reports: Discharge summary, nursing progress notes, etc; cardiac catheterization, ECG, radiology, and echo reports.
Although the database does not currently contain codes that indicate the procedures performed on a patient, the nursing notes, discharge summaries and charted parameters may be used to infer this information.
The MIMIC-II database supports a diverse range of analytic studies spanning epidemiology, clinical decision-rule development, and electronic tool development. Because the data are available online, along with a comprehensive user guide, it is hoped that an online community of MIMIC-II researchers will develop in which ideas can be exchanged and collaborations can develop.
A wide range of analyses have already been performed on these data, spanning epidemiology, clinical decision-rule development, and electronic tool development. For example, Saeed studied how certain ICU practices varied significantly as a function of time of day, ie, during the workday vs. the overnight shifts .
Jia et al assessed risk factors for acute respiratory distress syndrome in MIMIC-II patients who were mechanically ventilated for >48 hrs . Hug identified multivariate factors associated with death, successful wean of pressor infusions within 12 hrs, successful weans of intra-aortic balloon pumps, and development of septic hypotension; and he developed predictive statistical models for these outcomes . For a more detailed description of this dataset, please refer to our accompanying paper .
v2.6.0: released in April 2011.
This data project was approved by the Institutional Review Boards of Beth Israel Deaconess Medical Center (Boston, MA) and the Massachusetts Institute of Technology (Cambridge, MA). Requirement for individual patient consent was waived because the study did not impact clinical care and all protected health information was deidentified.
This project was supported by grant R01 EB001659 from the National Institute of Biomedical Imaging and Bioengineering and by support from Philips Healthcare. M.S. is employed by Philips Healthcare. M.V., L.-W.L., G.M., T.H. and R.G.M. received funding from the National Institutes of Health (NIH). A.T.R. consulted with General Electric Healthcare and received funding from the NIH.
Conflicts of Interest
The authors have no conflicts of interest to declare.
- M. Saeed, C. Lieu, G. Raber and R. G. Mark, "MIMIC II: a massive temporal ICU patient database to support research in intelligent patient monitoring," Computers in Cardiology, Memphis, TN, USA, 2002, pp. 641-644, https://doi.org/10.1109/CIC.2002.1166854.
- Moody G, Mark R. A database to support development and evaluation of intelligent intensive care monitoring. Comput Cardiol. 1996;33:657–660. [Google Scholar]
- Neamatullah I, Douglass M, Lehman LH, Reisner A, Villarroel M, Long WJ, Szolovits P, Moody GB, Mark RG, Clifford GD. Automated De-Identification of Free-Text Medical Records. BMC Medical Informatics and Decision Making, 2008, 8:32. doi:10.1186/1472-6947-8-32
- De-Identification Software Package. PhysioNet. https://doi.org/10.13026/C20M3F
- Saeed M. PhD thesis. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science; 2007. Temporal pattern recognition in multiparameter ICU data. Available at: http://dspace.mit.edu/handle/1721.1/40507
- Jia X, Malhotra A, Saeed M, et al. Risk factors for ARDS in patients receiving mechanical ventilation for >48 h. Chest. 2008;133:853–861
- Hug CW. PhD thesis. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science; 2009. Predicting the risk and trajectory of intensive care patients using survival models. Available at: http://dspace.mit.edu/handle/1721.1/33957
- Saeed M, Villarroel M, Reisner AT, Clifford G, Lehman LW, Moody G, Heldt T, Kyaw TH, Moody B, Mark RG. Multiparameter Intelligent Monitoring in Intensive Care II: a public-access intensive care unit database. Crit Care Med. 2011 May; 39(5):952-60. https://doi.org/10.1097/CCM.0b013e31820a92c6.
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