» Articles » PMID: 22256274

Open-access MIMIC-II Database for Intensive Care Research

Overview
Date 2012 Jan 19
PMID 22256274
Citations 37
Authors
Affiliations
Soon will be listed here.
Abstract

The critical state of intensive care unit (ICU) patients demands close monitoring, and as a result a large volume of multi-parameter data is collected continuously. This represents a unique opportunity for researchers interested in clinical data mining. We sought to foster a more transparent and efficient intensive care research community by building a publicly available ICU database, namely Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II). The data harnessed in MIMIC-II were collected from the ICUs of Beth Israel Deaconess Medical Center from 2001 to 2008 and represent 26,870 adult hospital admissions (version 2.6). MIMIC-II consists of two major components: clinical data and physiological waveforms. The clinical data, which include patient demographics, intravenous medication drip rates, and laboratory test results, were organized into a relational database. The physiological waveforms, including 125 Hz signals recorded at bedside and corresponding vital signs, were stored in an open-source format. MIMIC-II data were also deidentified in order to remove protected health information. Any interested researcher can gain access to MIMIC-II free of charge after signing a data use agreement and completing human subjects training. MIMIC-II can support a wide variety of research studies, ranging from the development of clinical decision support algorithms to retrospective clinical studies. We anticipate that MIMIC-II will be an invaluable resource for intensive care research by stimulating fair comparisons among different studies.

Citing Articles

Contribution of Open Access Databases to Intensive Care Medicine Research: Scoping Review.

Kallout J, Lamer A, Grosjean J, Kerdelhue G, Bouzille G, Clavier T J Med Internet Res. 2025; 27:e57263.

PMID: 39787600 PMC: 11757948. DOI: 10.2196/57263.


LieRHRV system for remote lie detection using heart rate variability parameters.

Davoodi M, Aspis N, Drori Y, Weiser-Bitoun I, Yaniv Y Sci Rep. 2024; 14(1):30749.

PMID: 39730487 PMC: 11681120. DOI: 10.1038/s41598-024-80480-5.


Inferring ECG Waveforms from PPG Signals with a Modified U-Net Neural Network.

Pinto R, De Oliveira H, Souto E, Giusti R, Veras R Sensors (Basel). 2024; 24(18).

PMID: 39338791 PMC: 11436109. DOI: 10.3390/s24186046.


An open-source framework for end-to-end analysis of electronic health record data.

Heumos L, Ehmele P, Treis T, Upmeier Zu Belzen J, Roellin E, May L Nat Med. 2024; 30(11):3369-3380.

PMID: 39266748 PMC: 11564094. DOI: 10.1038/s41591-024-03214-0.


Energy-Efficient PPG-Based Respiratory Rate Estimation Using Spiking Neural Networks.

Yang G, Kang Y, Charlton P, Kyriacou P, Kim K, Li L Sensors (Basel). 2024; 24(12).

PMID: 38931763 PMC: 11207339. DOI: 10.3390/s24123980.


References
1.
Li Q, Mark R, Clifford G . Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter. Physiol Meas. 2008; 29(1):15-32. PMC: 2259026. DOI: 10.1088/0967-3334/29/1/002. View

2.
Li Q, Mark R, Clifford G . Artificial arterial blood pressure artifact models and an evaluation of a robust blood pressure and heart rate estimator. Biomed Eng Online. 2009; 8:13. PMC: 2728101. DOI: 10.1186/1475-925X-8-13. View

3.
Lee J, Mark R . An investigation of patterns in hemodynamic data indicative of impending hypotension in intensive care. Biomed Eng Online. 2010; 9:62. PMC: 2976741. DOI: 10.1186/1475-925X-9-62. View

4.
Neamatullah I, Douglass M, Lehman L, Reisner A, Villarroel M, Long W . Automated de-identification of free-text medical records. BMC Med Inform Decis Mak. 2008; 8:32. PMC: 2526997. DOI: 10.1186/1472-6947-8-32. View

5.
Aboukhalil A, Nielsen L, Saeed M, Mark R, Clifford G . Reducing false alarm rates for critical arrhythmias using the arterial blood pressure waveform. J Biomed Inform. 2008; 41(3):442-51. PMC: 2504518. DOI: 10.1016/j.jbi.2008.03.003. View