ricu: R’s interface to intensive care data
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Nicolai Meinshausen | Peter Buhlmann | Nicolas Bennett | Drago Plevcko | Ida-Fong Ukor | N. Meinshausen | P. Bühlmann | Peter Buhlmann | Nicola Bennett | Drago Plečko | Ida-Fong Ukor | Drago Plevcko
[1] Mohammed Saeed,et al. Open-access MIMIC-II database for intensive care research , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[2] Marzyeh Ghassemi,et al. MIMIC-Extract: a data extraction, preprocessing, and representation pipeline for MIMIC-III , 2019, CHIL.
[3] M. Kuiper,et al. Cumulative lactate and hospital mortality in ICU patients , 2013, Annals of Intensive Care.
[4] Karsten M. Borgwardt,et al. Early prediction of circulatory failure in the intensive care unit using machine learning , 2020, Nature Medicine.
[5] Tom J. Pollard,et al. A Comparative Analysis of Sepsis Identification Methods in an Electronic Database* , 2018, Critical care medicine.
[6] A. Dreher. Modeling Survival Data Extending The Cox Model , 2016 .
[7] R. Bellomo,et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). , 2016, JAMA.
[8] Laura E. Barnes,et al. Predictive Models of Sepsis in Adult ICU Patients , 2018, 2018 IEEE International Conference on Healthcare Informatics (ICHI).
[9] G. Moody,et al. A database to support development and evaluation of intelligent intensive care monitoring , 1996, Computers in Cardiology 1996.
[10] G. Clermont,et al. Sharing ICU Patient Data Responsibly Under the Society of Critical Care Medicine/European Society of Intensive Care Medicine Joint Data Science Collaboration: The Amsterdam University Medical Centers Database (AmsterdamUMCdb) Example* , 2021, Critical care medicine.
[11] Peter Szolovits,et al. MIMIC-III, a freely accessible critical care database , 2016, Scientific Data.
[12] C. Sprung,et al. The ACCP-SCCM consensus conference on sepsis and organ failure. , 1992, Chest.
[13] Shamim Nemati,et al. An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU , 2017, Critical care medicine.
[14] Michael Bailey,et al. Dynamic lactate indices as predictors of outcome in critically ill patients , 2011, Critical care.
[15] T. Lange,et al. Severe hyperlactatemia, lactate clearance and mortality in unselected critically ill patients , 2016, Intensive Care Medicine.
[16] U. Kyriacos,et al. Validation of a modified early warning score‐linked Situation‐Background‐Assessment‐Recommendation communication tool: A mixed methods study , 2017, Journal of clinical nursing.
[17] Alistair E. W. Johnson,et al. The eICU Collaborative Research Database, a freely available multi-center database for critical care research , 2018, Scientific Data.
[18] Katherine A. Heller,et al. An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection , 2017, MLHC.
[19] Mark Hoogendoorn,et al. Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy , 2020, Intensive Care Medicine.
[20] Mike Jones,et al. NEWSDIG: The National Early Warning Score Development and Implementation Group. , 2012, Clinical medicine.
[21] R S Evans,et al. Electronic Health Records: Then, Now, and in the Future , 2016, Yearbook of Medical Informatics.
[22] Hye Jin Kam,et al. Learning representations for the early detection of sepsis with deep neural networks , 2017, Comput. Biol. Medicine.
[23] Paul Brown,et al. Closing the data loop: An integrated open access analysis platform for the MIMIC database , 2016, 2016 Computing in Cardiology Conference (CinC).
[24] L. Blanch,et al. A universal definition of ARDS: the PaO2/FiO2 ratio under a standard ventilatory setting—a prospective, multicenter validation study , 2013, Intensive Care Medicine.
[25] Brian W. Pickering,et al. Data Utilization for Medical Decision Making at the Time of Patient Admission to ICU* , 2013, Critical care medicine.
[26] C. Subbe,et al. Validation of a modified Early Warning Score in medical admissions. , 2001, QJM : monthly journal of the Association of Physicians.
[27] Jeroen Ooms,et al. The jsonlite Package: A Practical and Consistent Mapping Between JSON Data and R Objects , 2014, ArXiv.
[28] Haipeng Shen,et al. Artificial intelligence in healthcare: past, present and future , 2017, Stroke and Vascular Neurology.
[29] Uli K. Chettipally,et al. Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach , 2016, JMIR medical informatics.
[30] Edzer Pebesma,et al. Measurement Units in R , 2016, R J..
[31] J. Donnelly,et al. External Validation of a Widely Implemented Proprietary Sepsis Prediction Model in Hospitalized Patients. , 2021, JAMA internal medicine.
[32] J. Vincent,et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure , 1996, Intensive Care Medicine.