Multiparameter Intelligent Monitoring in Intensive Care II: A public-access intensive care unit database*
Open accessAbstract
OBJECTIVE: We sought to develop an intensive care unit research database applying automated techniques to aggregate high-resolution diagnostic and therapeutic data from a large, diverse population of adult intensive care unit patients. This freely available database is intended to support epidemiologic research in critical care medicine and serve as a resource to evaluate new clinical decision support and monitoring algorithms. DESIGN: Data collection and retrospective analysis. SETTING: All adult intensive care units (medical intensive care unit, surgical intensive care unit, cardiac care unit, cardiac surgery recovery unit) at a tertiary care hospital. PATIENTS: Adult patients admitted to intensive care units between 2001 and 2007. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) database consists of 25,328 intensive care unit stays. The investigators collected 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, and, for a subset of patients, high-resolution vital sign trends and waveforms. Data were automatically deidentified to comply with Health Insurance Portability and Accountability Act standards and integrated with relational database software to create electronic intensive care unit records for each patient stay. The data were made freely available in February 2010 through the Internet along with a detailed user's guide and an assortment of data processing tools. The overall hospital mortality rate was 11.7%, which varied by critical care unit. The median intensive care unit length of stay was 2.2 days (interquartile range, 1.1-4.4 days). According to the primary International Classification of Diseases, 9th Revision codes, the following disease categories each comprised at least 5% of the case records: diseases of the circulatory system (39.1%); trauma (10.2%); diseases of the digestive system (9.7%); pulmonary diseases (9.0%); infectious diseases (7.0%); and neoplasms (6.8%). CONCLUSIONS: MIMIC-II documents a diverse and very 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 new public-access resource for critical care research, supporting a diverse range of analytic studies spanning epidemiology, clinical decision-rule development, and electronic tool development.
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MIMIC-II followed with a significantly increased sample size and breadth of information due to the clinical information being entirely sourced from various digital information systems.
“MIMIC-II followed with a significantly increased sample size and breadth of information due to the clinical information being entirely sourced from various digital information systems2.”
From MIMIC-IV, a freely accessible electronic health record dataset (2023) · cites this paper as [2]
Materials and Methods (Database Development and Clinical Data; page unavailable) says MIMIC-II aggregated clinical data from ICU information systems and hospital electronic archives, while the Conclusions describe a large, detailed database. The cited paper does not establish the claimed increase in sample size and information breadth relative to its predecessor or attribute that increase to the use of digital systems. (LLM, conf=0.85)
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- Authors
- Saeed, M., Villarroel, M., Reisner, A. T., Clifford, G., Lehman, L.-W., Moody, G., Heldt, T., Kyaw, T. H., Moody, B., & Mark, R. G.
- Affiliations
- Massachusetts Institute of Technology (US)
- Beth Israel Deaconess Medical Center (US)
- ORCID
- Venue
- Critical Care Medicine
- Published
- 2011-05
- Volume
- 39
- Issue
- 5
- Pages
- 952-960
- ISSN
- 0090-3493 (ISSN-L)
- PMC
- PMC3124312
- OpenAlex
- W2046788142
- Cited by
- 1,162
- References
- 25
- Citations by year
- 2026: 26 · 2025: 58 · 2024: 60 · 2023: 84 · 2022: 86 · 2021: 82 · 2020: 93 · 2019: 83 · 2018: 91 · 2017: 80 · 2016: 133 · 2015: 118 · 2014: 87 · 2013: 40 · 2012: 31
- FWCI
- 54.29
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Critical Care Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Funders
- National Institute of Biomedical Imaging and Bioengineering (R01EB001659, R01-EB017205)
- Topics
- Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
- Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- MIMIC-II, critical care research, electronic health records, physiological waveforms, clinical decision support, in-hospital mortality, ICU length of stay, de-identification, ventilator settings, vasoactive drugs
- MeSH terms
- Adult, Artificial Intelligence, Critical Care, Expert Systems, Female, Humans, Intensive Care Units, Medical Informatics Applications, Monitoring, Physiologic, Quality Control, Retrospective Studies, United States, Databases, Factual, Medical Records Systems, Computerized, Decision Support Systems, Clinical
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