Paper Info
Title
A Bayesian network for early diagnosis of sepsis patients: a basis for a clinical decision support system
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.
Year
DOI
Venue
2012
10.1109/ICCABS.2012.6182636
Computational Advances in Bio and Medical Sciences
Keywords
Field
DocType
electronic medical record,sepsis patient,bayesian network,inordinate immune response,severe stage,severe medical condition,clinical decision support system,resulting network,lactate level,early diagnosis,bayesian inference network,sepsis symptom,cluster analysis,cdss,bayesian inference,clustering,emr,biomechanics,decision support systems,immune response
Disease,Systemic inflammatory response syndrome,Biology,Septic shock,Intensive care medicine,Bayesian network,Medical record,Clinical decision support system,Bioinformatics,Sepsis,Mean arterial pressure
Conference
ISBN
Citations 
PageRank 
978-1-4673-1319-3
4
0.65
References 
Authors
2
4
Name
Order
Citations
PageRank
Eren Gultepe1211.61
Hien V. Nguyen284977.92
Timothy Albertson340.65
Ilias Tagkopoulos4709.30