Journal
MATHEMATICAL BIOSCIENCES
Volume 284, Issue -, Pages 12-20Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.mbs.2016.11.004
Keywords
SIRS; Sepsis; Bayesian network; EM learning; Prediction
Categories
Ask authors/readers for more resources
The aim of this paper is to apply machine learning as a method to refine a manually constructed CPN for the assessment of the severity of the systemic inflammatory response syndrome (SIRS). The goal of tuning the CPN is to create a scoring system that uses only objective data, compares favourably with other severity-scoring systems and differentiates between sepsis and non-infectious SIRS. The resulting model, the Learned-Age (L-A) -Sepsis CPN has good discriminatory ability for the prediction of 30-day mortality with an area under the ROC curve of 0.79. This result compares well to existing scoring systems. The L-A-Sepsis CPN also has a modest ability to discriminate between sepsis and non-infectious SIRS. (C) 2016 Elsevier Inc. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available