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 (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.