A Bayesian Approach to Model-Development: Automatic Learning for Tuning Predictive Performance. Issue 20 (2015)

• Record Type: Journal Article
• Authors: Ward, Logan; Andreassen, Steen
• Is Part Of: IFAC-PapersOnLine. Volume 48:Issue 20(2015)
• Page Start: 481
• Page End: 486
• Publication Date: 2015
• Subjects: Machine learning -- Biological and medical system modelling -- System identification and validation; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2015.10.187
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: 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).