This paper is in the following e-collection/theme issue:

Decision Support for Health Professionals (1185) Electronic Health Records (1053)

Published on in Vol 4, No 3 (2016): Jul-Sept

Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach

Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach

Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach

Authors of this article:

Thomas Desautels1 Author Orcid Image ;   Jacob Calvert1 Author Orcid Image ;   Jana Hoffman1 Author Orcid Image ;   Melissa Jay1 Author Orcid Image ;   Yaniv Kerem2, 3 Author Orcid Image ;   Lisa Shieh4 Author Orcid Image ;   David Shimabukuro5 Author Orcid Image ;   Uli Chettipally6, 7 Author Orcid Image ;   Mitchell D Feldman8 Author Orcid Image ;   Chris Barton7 Author Orcid Image ;   David J Wales9 Author Orcid Image ;   Ritankar Das1 Author Orcid Image
Article Authors Cited by (313) Tweetations (8) Metrics

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  243. Desautels T, Das R, Calvert J, Trivedi M, Summers C, Wales D, Ercole A. Prediction of early unplanned intensive care unit readmission in a UK tertiary care hospital: a cross-sectional machine learning approach. BMJ Open 2017;7(9):e017199 View
  244. Shimabukuro D, Barton C, Feldman M, Mataraso S, Das R. Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial. BMJ Open Respiratory Research 2017;4(1):e000234 View
  245. Das P, Wiese L, Mast M, Böhnke J, Wulff A, Marschollek M, Bode L, Rathert H, Jack T, Schamer S, Beerbaum P, Rübsamen N, Karch A, Groszweski-Anders C, Haller A, Frank T. An attention-based bidirectional LSTM-CNN architecture for the early prediction of sepsis. International Journal of Data Science and Analytics 2024 View
  246. Pérez-Tome J, Parrón-Carreño T, Castaño-Fernández A, Nievas-Soriano B, Castro-Luna G. Sepsis mortality prediction with Machine Learning Tecniques. Medicina Intensiva (English Edition) 2024;48(10):584 View
  247. Scott I, De Guzman K, Falconer N, Canaris S, Bonilla O, McPhail S, Marxen S, Van Garderen A, Abdel-Hafez A, Barras M. Evaluating automated machine learning platforms for use in healthcare. JAMIA Open 2024;7(2) View
  248. Kuan L, Chin L, De L, Cheng C, Tuao Z, Zixian Y, Roy D, Roy D. Predictive Analysis of Patient Risk of Death in ICU: A Bibliometric Analysis. SHS Web of Conferences 2024;194:01005 View
  249. Kaur I, Ahmad T, Doja M. A Systematic Review of Medical Expert Systems for Cardiac Arrest Prediction. Current Bioinformatics 2024;19(6):551 View
  250. Schmulevich D, Hynes A, Murali S, Benjamin A, Cannon J. Optimizing damage control resuscitation through early patient identification and real‐time performance improvement. Transfusion 2024;64(8):1551 View
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  254. Matos J, Gallifant J, Chowdhury A, Economou-Zavlanos N, Charpignon M, Gichoya J, Celi L, Nazer L, King H, Wong A. A Clinician’s Guide to Understanding Bias in Critical Clinical Prediction Models. Critical Care Clinics 2024;40(4):827 View
  255. Zeydan E, Arslan S, Liyanage M. Managing Distributed Machine Learning Lifecycle for Healthcare Data in the Cloud. IEEE Access 2024;12:115750 View
  256. Özmen E, Emir B. The Role of Machine Learning Algorithms in Sepsis Diagnosis: A Retrospective Overview using Bibliometric Analysis. OSMANGAZİ JOURNAL OF MEDICINE 2024;46(6) View
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Books/Policy Documents

  1. Bulgarelli L, Núñez-Reiz A, Deliberato R. Leveraging Data Science for Global Health. View
  2. Sharma N, Gautam S, Henry A, Kumar A. Machine Learning and Big Data. View
  3. Xie J, Coopersmith C. Handbook of Sepsis. View
  4. Berikol G, Berikol G. Artificial Intelligence in Precision Health. View
  5. Cerrato P, Halamka J. The Transformative Power of Mobile Medicine. View
  6. Thomas M, Abraham D, Liu D. Interdisciplinary Approaches to Digital Transformation and Innovation. View
  7. Chaudhary P, Gupta D, Singh S. Advances in Communication and Computational Technology. View
  8. Bock C, Moor M, Jutzeler C, Borgwardt K. Artificial Neural Networks. View
  9. Pérez-Fernández J, Raimondi N, Murillo Cabezas F. Critical Care Administration. View
  10. Sagi T, Shmueli N, Friedman B, Bergman R. Heterogeneous Data Management, Polystores, and Analytics for Healthcare. View
  11. Sailaja N, Yelamarthi M, Chandana Y, Karadi P, Yedla S. Machine Learning Technologies and Applications. View
  12. Silva J, Villareal-González R, Varela N, Maco J, Villón M, Marín–González F, Lezama O. Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications. View
  13. Nayyar A, Gadhavi L, Zaman N. Machine Learning and the Internet of Medical Things in Healthcare. View
  14. Nesaragi N, Patidar S. Infections and Sepsis Development. View
  15. Sa M, Crespo R. The Sepsis Codex. View
  16. Luo K, Li J, Zhao Y. LISS 2021. View
  17. Rayan Z, Alfonse M, Salem A. Digital Transformation Technology. View
  18. Schinkel M, Paranjape K, Nanayakkara P, Wiersinga W. The Sepsis Codex. View
  19. Hermelin T, Singer P, Rappoport N. Artificial Intelligence in Medicine. View
  20. Tsang W, Benoit D. Living Beyond Data. View
  21. Sharma A, Dasgupta D, Bose S, Misra U, Pahari I, Karmakar R, Pal S. Proceedings of International Conference on Computational Intelligence, Data Science and Cloud Computing. View
  22. Rayan Z, Alfonse M, Salem A. Digital Transformation Technology. View
  23. Ibáñez-Redin G, Duarte O, Cagnani G, Oliveira O. Machine Learning for Advanced Functional Materials. View
  24. Lydia E, Althubiti S, Anupama C, Kumar K. Intelligent Data Engineering and Analytics. View
  25. Shanthi N, Aadhishri A, Suganthe R, Gao X. Computational Sciences and Sustainable Technologies. View
  26. Winter A, Kirsten T, Hartwig M. Biomedical Engineering Systems and Technologies. View
  27. Jain D, Gupta A, Pandey A, Vats P. Reshaping Intelligent Business and Industry. View