Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial.

Journal: BMJ open respiratory research
Published Date: Nov 9, 2017

Abstract

INTRODUCTION: Several methods have been developed to electronically monitor patients for severe sepsis, but few provide predictive capabilities to enable early intervention; furthermore, no severe sepsis prediction systems have been previously validated in a randomised study. We tested the use of a machine learning-based severe sepsis prediction system for reductions in average length of stay and in-hospital mortality rate.

Authors

  • David W Shimabukuro
    Division of Critical Care Medicine, Department of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, California, USA.
  • Christopher W Barton
    Department of Emergency Medicine, University of California San Francisco, San Francisco, California, USA.
  • Mitchell D Feldman
    Division of General Internal Medicine, Department of Medicine, University of California San Francisco, San Francisco, California, USA.
  • Samson J Mataraso
    Department of Bioengineering, University of California Berkeley, Berkeley, California, USA.
  • Ritankar Das
    Dascena, Inc, Hayward, California, USA.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals