Automatic learning of mortality in a CPN model of the systemic inflammatory response syndrome.
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 (LA) -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 LA-Sepsis CPN also has a modest ability to discriminate between sepsis and non-infectious SIRS.
Centre for Model-based Medical Decision Support, Aalborg University, Fredrik Bajers Vej 7 E4, 9220 Aalborg Ø, Denmark. Electronic address: lmw@hst.aau.dk.
Logan
L
Ward
Unit of Infectious Diseases, Rambam Health Care Campus, HaAliya HaShniya St 8, Haifa, Israel. Electronic address: m_paul@rambam.health.gov.il.
Mical
M
Paul
Centre for Model-based Medical Decision Support, Aalborg University, Fredrik Bajers Vej 7 E4, 9220 Aalborg Ø, Denmark; Treat Systems, Hasserisvej 125, 9000 Aalborg, Denmark. Electronic address: sa@hst.aau.dk.
Steen
S
Andreassen
D001499:Bayes Theorem; D006801:Humans; D008962:Models, Theoretical; D018746:Systemic Inflammatory Response Syndrome
Bayesian network; EM learning; Prediction; SIRS; Sepsis