MIMIC II: a massive temporal ICU patient database to support research in intelligent patient monitoring

By Mohammed Saeed, C. Lieu, G. Raber, R. G. Mark. Computers in cardiology, 2002

311 citations recorded by the source corpus.

Subfields: Surgery

View Paper: https://www.semanticscholar.org/paper/88a981749212f3dcbba91bc626f1c69fcb7bc54b

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In Surgery

1. Shamim Nemati
2. Marzyeh Ghassemi
3. G. Clifford
4. David C. Kale
5. Randall C. Wetzel
6. Leo Anthony Celi
7. Alistair E. W. Johnson
8. Suchi Saria

Citations in the corpus

• Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives (2018, methodology): “With the growing adoption rate of EHRs [2], researchers gain access to rich data sets, such as the Medical Information Mart for Intensive Care (MIMIC) database [3, 4], and the Informatics for Integrating Biology and the Bedside (i2b2) datamarts [5–10].”
• Machine Learning and Decision Support in Critical Care (2016, methodology): “A notable success in the release of data in critical care is the PhysioBank component of PhysioNet [20], and in particular the Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) database [21], [22].”
• Automated De-Identification of Free-Text Medical Records (2014, background): “In the future, this annotated database will provide a research resource for the development of intelligent ICU monitoring systems [2].”
• A Bayesian Nonparametric Approach for Estimating Individualized Treatment-Response Curves (2016, methodology): “The data are publicly available in the MIMIC (Multiparameter Intelligent Monitoring in Intensive Care)-II Clinical Database (Saeed et al., 2002).”
• Learning (predictive) risk scores in the presence of censoring due to interventions (2015, methodology): “Dataset: We use MIMIC-II, a publicly available dataset containing electronic health record data from patients admitted to the ICUs at the Beth Israel Deaconess Medical Center from 2001 to 2008 (Saeed et al, 2002, 2011).”
• Learning Treatment-Response Models from Multivariate Longitudinal Data (2017, methodology): “Dataset: We apply and evaluate our model on the MIMIC II Clinical Database (Saeed et al., 2002), which consists of electronic health records for ICU patients from the Beth Israel Deaconess Medical Center.”
• Predicting Complications in Critical Care Using Heterogeneous Clinical Data (2016, methodology): “The source of our data is MIMIC II [41], a publicly available database, part of Physionet [42], containing physiological signals and clinical data of more than 2300 patients in Critical Care.”
• Unsupervised pattern discovery in electronic health care data using probabilistic clustering models (2012, background): “…care unit (ICU) Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro.t or commercial advantage and that copies bear this notice and the full citation on the .rst page.”
• A Bayesian Nonparametic Approach for Estimating Individualized Treatment-Response Curves (2016, methodology): “The data are publicly available in the MIMIC-II Clinical Database (Saeed et al., 2002).”
• Big Data Analytics in Healthcare (2015, background): “[97] Mohammed Saeed, C Lieu, G Raber, and RG Mark.”