Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Monday, December 24 through Wednesday, December 26 inclusive. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Citing this Article

Right click to copy or hit: ctrl+c (cmd+c on mac)

Published on 30.09.16 in Vol 4, No 3 (2016): Jul-Sept

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

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

According to Crossref, the following articles are citing this article (DOI 10.2196/medinform.5909):

(note that this is only a small subset of citations)

  1. Lovejoy CA, Buch V, Maruthappu M. Artificial intelligence in the intensive care unit. Critical Care 2019;23(1)
    CrossRef
  2. Delahanty RJ, Alvarez J, Flynn LM, Sherwin RL, Jones SS. Development and Evaluation of a Machine Learning Model for the Early Identification of Patients at Risk for Sepsis. Annals of Emergency Medicine 2019;
    CrossRef
  3. Ruminski CM, Clark MT, Lake DE, Kitzmiller RR, Keim-Malpass J, Robertson MP, Simons TR, Moorman JR, Calland JF. Impact of predictive analytics based on continuous cardiorespiratory monitoring in a surgical and trauma intensive care unit. Journal of Clinical Monitoring and Computing 2018;
    CrossRef
  4. Sinha M, Jupe J, Mack H, Coleman TP, Lawrence SM, Fraley SI. Emerging Technologies for Molecular Diagnosis of Sepsis. Clinical Microbiology Reviews 2018;31(2)
    CrossRef
  5. Kapoor R, Walters SP, Al-Aswad LA. The current state of artificial intelligence in ophthalmology. Survey of Ophthalmology 2018;
    CrossRef
  6. Kulabukhov VV, Kudryavtsev AN, Kleuzovich AA, Chizhov AG, Raevskaya MB. Diagnostic value of molecular biomarkers of infection in screening by Sepsis-3 criteria. Khirurgiya. Zhurnal im. N.I. Pirogova 2018;(5):58
    CrossRef
  7. Núñez Reiz A, Armengol de la Hoz M, Sánchez García M. Big Data Analysis y Machine Learning en medicina intensiva. Medicina Intensiva 2018;
    CrossRef
  8. Meiring C, Dixit A, Harris S, MacCallum NS, Brealey DA, Watkinson PJ, Jones A, Ashworth S, Beale R, Brett SJ, Singer M, Ercole A, Celi LA. Optimal intensive care outcome prediction over time using machine learning. PLOS ONE 2018;13(11):e0206862
    CrossRef
  9. Davoodi R, Moradi MH. Mortality prediction in intensive care units (ICUs) using a deep rule-based fuzzy classifier. Journal of Biomedical Informatics 2018;79:48
    CrossRef
  10. Dummitt B, Zeringue A, Palagiri A, Veremakis C, Burch B, Yount B. Using survival analysis to predict septic shock onset in ICU patients. Journal of Critical Care 2018;48:339
    CrossRef
  11. Hong WS, Haimovich AD, Taylor RA, Dong Q. Predicting hospital admission at emergency department triage using machine learning. PLOS ONE 2018;13(7):e0201016
    CrossRef
  12. Nemati S, Holder A, Razmi F, Stanley MD, Clifford GD, Buchman TG. An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU. Critical Care Medicine 2018;46(4):547
    CrossRef
  13. Bock C, Gumbsch T, Moor M, Rieck B, Roqueiro D, Borgwardt K. Association mapping in biomedical time series via statistically significant shapelet mining. Bioinformatics 2018;34(13):i438
    CrossRef
  14. Woods JS, Saxena M, Nagamine T, Howell RS, Criscitelli T, Gorenstein S, M. Gillette B. The Future of Data-Driven Wound Care. AORN Journal 2018;107(4):455
    CrossRef
  15. Pruinelli L, Westra BL, Yadav P, Hoff A, Steinbach M, Kumar V, Delaney CW, Simon G. Delay Within the 3-Hour Surviving Sepsis Campaign Guideline on Mortality for Patients With Severe Sepsis and Septic Shock*. Critical Care Medicine 2018;46(4):500
    CrossRef
  16. Jouffroy R, Saade A, Ellouze S, Carpentier A, Michaloux M, Carli P, Vivien B. Prehospital triage of septic patients at the SAMU regulation: Comparison of qSOFA, MRST, MEWS and PRESEP scores. The American Journal of Emergency Medicine 2018;36(5):820
    CrossRef
  17. Rush B, Celi LA, Stone DJ. Applying machine learning to continuously monitored physiological data. Journal of Clinical Monitoring and Computing 2018;
    CrossRef
  18. Pirracchio R, Cohen MJ, Malenica I, Cohen J, Chambaz A, Cannesson M, Lee C, Resche-Rigon M, Hubbard A. Big data and targeted machine learning in action to assist medical decision in the ICU. Anaesthesia Critical Care & Pain Medicine 2018;
    CrossRef
  19. Weldon DL, Kowalski R, Schubel L, Schuchardt B, Arnold R, Capan M, Blumenthal J, Franklin E, Catchpole K, Jacob Seagull F, Sanford Schwartz J, Miller K. Signaling Sepsis Scenario Development & Validation. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2018;62(1):615
    CrossRef
  20. Mohamadlou H, Lynn-Palevsky A, Barton C, Chettipally U, Shieh L, Calvert J, Saber NR, Das R. Prediction of Acute Kidney Injury With a Machine Learning Algorithm Using Electronic Health Record Data. Canadian Journal of Kidney Health and Disease 2018;5:205435811877632
    CrossRef
  21. Evangelatos N, Bauer P, Reumann M, Satyamoorthy K, Lehrach H, Brand A. Metabolomics in Sepsis and Its Impact on Public Health. Public Health Genomics 2017;20(5):274
    CrossRef
  22. Calvert J, Hoffman J, Barton C, Shimabukuro D, Ries M, Chettipally U, Kerem Y, Jay M, Mataraso S, Das R. Cost and mortality impact of an algorithm-driven sepsis prediction system. Journal of Medical Economics 2017;20(6):646
    CrossRef
  23. Shashikumar SP, Li Q, Clifford GD, Nemati S. Multiscale network representation of physiological time series for early prediction of sepsis. Physiological Measurement 2017;38(12):2235
    CrossRef
  24. Shashikumar SP, Stanley MD, Sadiq I, Li Q, Holder A, Clifford GD, Nemati S. Early sepsis detection in critical care patients using multiscale blood pressure and heart rate dynamics. Journal of Electrocardiology 2017;50(6):739
    CrossRef
  25. McCoy A, Das R. Reducing patient mortality, length of stay and readmissions through machine learning-based sepsis prediction in the emergency department, intensive care unit and hospital floor units. BMJ Open Quality 2017;6(2):e000158
    CrossRef
  26. Park E, Chang H, Nam HS. Use of Machine Leaning Classifiers and Sensor Data to Detect Neurological Deficit in Stroke Patients. Journal of Medical Internet Research 2017;19(4):e120
    CrossRef
  27. Das R, Wales DJ. Machine learning landscapes and predictions for patient outcomes. Royal Society Open Science 2017;4(7):170175
    CrossRef
  28. Salluh JIF, Soares M, Keegan MT. Understanding intensive care unit benchmarking. Intensive Care Medicine 2017;43(11):1703
    CrossRef
  29. Desautels T, Calvert J, Hoffman J, Mao Q, Jay M, Fletcher G, Barton C, Chettipally U, Kerem Y, Das R. Using Transfer Learning for Improved Mortality Prediction in a Data-Scarce Hospital Setting. Biomedical Informatics Insights 2017;9:117822261771299
    CrossRef

According to Crossref, the following books are citing this article (DOI 10.2196/medinform.5909)

:
  1. Xie J, Coopersmith CM. Handbook of Sepsis. 2018. Chapter 16:253
    CrossRef