The eICU Collaborative Research Database, a freely available multi-center database for critical care research

Keywords

Database, Documentation, Intensive care unit, Computer science, Critically ill

References

• The CITI Program: An International Online Resource for Education in Human Subjects Protection and the Responsible Conduct of ResearchPaul G. Braunschweiger, Kenneth W. Goodman — 2007
• An efficient record linkage scheme using graphical analysis for identifier error detectionE.A. Bretz — 2011
• Machine Learning and Decision Support in Critical CareAlistair E. W. Johnson, Mohammad M. Ghassemi, Shamim Nemati — 2016
• Acute Physiology and Chronic Health Evaluation (APACHE) IV: Hospital mortality assessment for today’s critically ill patients*Jack E. Zimmerman, Andrew A. Kramer, Douglas McNair — 2006
• The eICU Research Institute - A Collaboration Between Industry, Health-Care Providers, and AcademiaMichael McShea, Randy Holl, Omar Badawi — 2010
• Critical care and the global burden of critical illness in adultsNeill K. J. Adhikari, Robert Fowler, Satish Bhagwanjee — 2010
• IPython: A System for Interactive Scientific ComputingFernando Pérez, Brian Granger — 2007
• A multicenter study of ICU telemedicine reengineering of adult critical care.Craig M. Lilly, John M. McLaughlin, Huifang Zhao — 2014
• Best Practices for Scientific ComputingGreg Wilson, D. A. Aruliah, C. Titus Brown — 2012
• Intensive care medicine is 60 years old: the history and future of the intensive care unit.F. E. Kelly, Kevin Fong, Nicholas Hirsch — 2014
• MIMIC-III, a freely accessible critical care databaseAlistair E. W. Johnson, Tom Pollard, Lu Shen — 2016
• Reproducibility in critical care: a mortality prediction case studyAlistair E. W. Johnson, Tom Pollard, Roger G. Mark — 2017
• tableone: An open source Python package for producing summary statistics for research papersTom Pollard, Alistair E. W. Johnson, Jesse D. Raffa — 2018
• Jupyter Notebooks-a publishing format for reproducible computational workflowsKluyver, Thomas, Ragan-Kelley, Benjamin, Perez, Fernando — 2016
• MIT-LCP/eicu-code: eICU-CRD Code Repository v1.0Tom Pollard, Alistair Johnson, obadawi — 2018
• Integration of tools for binding archetypes to SNOMED CTErik Sundvall, Rahil Qamar, Mikael Nyström — 2008
• PhysioBank, PhysioToolkit, and PhysioNetAry L. Goldberger, Luı́s A. Nunes Amaral, Leon Glass — 2000
• Automated de-identification of free-text medical recordsIshna Neamatullah, Margaret Douglass, Li-wei H. Lehman — 2008
• The eICU Collaborative Research DatabasePollard, Tom Joseph, Johnson, Alistair Edward William, Raffa, Jesse — 2017
• Jupyter Notebooks - a publishing format for reproducible computational workflowsT. Kluyver, B. Ragan-Kelley, Fernando Pérez — 2016

Cited by

• Development of a model for predicting mortality of breast cancer admitted to Intensive Care UnitLiana Spulber — 2022
• Towards a decision support tool for intensive care discharge: machine learning algorithm development using electronic healthcare data from MIMIC-III and Bristol, UKChris McWilliams, Daniel J. Lawson, Raúl Santos‐Rodríguez — 2019
• Multivariate Time-Series Similarity Assessment via Unsupervised Representation Learning and Stratified Locality Sensitive Hashing: Application to Early Acute Hypotensive Episode DetectionJwala Dhamala, Emmanuel Azuh, Abdullah Al-Dujaili — 2018
• An evaluation of the influence of body mass index on severity scoring.Rodrigo Octávio Deliberato, Ary Serpa Neto, Matthieu Komorowski — 2019
• FADL: Federated-Autonomous Deep Learning for Distributed Electronic Health RecordDianbo Liu, Timothy A. Miller, Raheel Sayeed — 2018
• Privacy-Preserving Distributed Deep Learning for Clinical DataBrett K. Beaulieu‐Jones, William Yuan, Samuel G. Finlayson — 2018
• The Connected Intensive Care Unit Patient: Exploratory Analyses and Cohort Discovery From a Critical Care Telemedicine DatabasePatrick Essay, Tala B. Shahin, Baran Balkan — 2019
• Telemedicine in the ICU: clinical outcomes, economic aspects, and trainee education.C Becker, Maria Fusaro, Corey Scurlock — 2019
• A Different Type of "Obesity Paradox".Andrew A. Kramer — 2019
• Understanding the Artificial Intelligence Clinician and optimal treatment strategies for sepsis in intensive careMatthieu Komorowski, Leo Anthony Celi, Omar Badawi — 2019
• Intensive Care Unit Telemedicine in the Era of Big Data, Artificial Intelligence, and Computer Clinical Decision Support Systems.Ryan Kindle, Omar Badawi, Leo Anthony Celi — 2019
• Machine learning for early prediction of circulatory failure in the intensive care unitStephanie L. Hyland, Martin Faltys, Matthias Hüser — 2019
• Data-driven discovery of a novel sepsis pre-shock state predicts impending septic shock in the ICURan Liu, Joseph L. Greenstein, Stephen J. Granite — 2019
• Association of hypokalemia with an increased risk for medically treated arrhythmiasColin T. Phillips, Junmei Wang, Leo Anthony Celi — 2019
• Graph Convolutional Transformer: Learning the Graphical Structure of Electronic Health RecordsEdward Choi, Zhen Xu, Yujia Li — 2019
• Critical Care, Critical DataChristopher V. Cosgriff, Leo Anthony Celi, David J. Stone — 2019
• Prognostic value of serum lactate kinetics in critically ill patients with cirrhosis and acute-on-chronic liver failure: a multicenter studyFeng Gao, Xielin Huang, Mengxing Cai — 2019
• Measuring variability between clusters by subgroup: An extension of the median odds ratioChristopher J. Yarnell, Ruxandra Pinto, Rob Fowler — 2019
• Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning TasksBret Nestor, Matthew B. A. McDermott, Willie Boag — 2019
• Reproducibility in Machine Learning for HealthMatthew B. A. McDermott, Shirly Wang, Nikki Marinsek — 2019

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