A Bayesian network for early diagnosis of sepsis patients: A basis for a clinical decision support system

Eren Gultepe, Hien H Nguyen, Timothy E Albertson, Ilias Tagkopoulos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Sepsis is a severe medical condition caused by an inordinate immune response to an infection. Early detection of sepsis symptoms is important to prevent the progression into the more severe stages of the disease, which kills one in four it effects. Electronic medical records of 1492 patients containing 233 cases of sepsis were used in a clustering analysis to identify features that are indicative of sepsis and can be further used for training a Bayesian inference network. The Bayesian network was constructed using the systemic inflammatory response syndrome criteria, mean arterial pressure, and lactate levels for sepsis patients. The resulting network reveals a clear correlation between lactate levels and sepsis. Furthermore, it was shown that lactate levels may be predicative of the SIRS criteria. In this light, Bayesian networks of sepsis patients hold the promise of providing a clinical decision support system in the future.

Original languageEnglish (US)
Title of host publication2012 IEEE 2nd International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2012
DOIs
StatePublished - 2012
Event2012 IEEE 2nd International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2012 - Las Vegas, NV, United States
Duration: Feb 23 2012Feb 25 2012

Other

Other2012 IEEE 2nd International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2012
CountryUnited States
CityLas Vegas, NV
Period2/23/122/25/12

Keywords

  • Bayesian Network
  • CDSS
  • Clustering
  • Decisison support system
  • EMR
  • Sepsis
  • Septic shock
  • Severe Sepsis

ASJC Scopus subject areas

  • Biomedical Engineering
  • Applied Mathematics

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