Open-access MIMIC-II Database for Intensive Care Research
Overview
Affiliations
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.
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.