A Bayesian Approach to Model-Development: Automatic Learning for Tuning Predictive Performance

The value of manually constructed and tuned Bayesian networks has been demonstrated empirically, however this informal process is limited in terms of what can be reasonably achieved. This paper presents the application of a formal machine learning process, EM learning, to a manually constructed CPN for the assessment of the severity of sepsis. Through learning, the model is tuned to predict 30-day mortality, and displays a significant improvement in discriminatory ability assessed by area under the ROC curve (previous model AUC = 0.647, new model AUC = 0.739, p<0.001).