文章基本信息

标题：A Bayesian Approach to Model-Development: Automatic Learning for Tuning Predictive Performance
本地全文：下载
作者：Logan Ward ; Steen Andreassen
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2015
卷号：48
期号：20
页码：481-486
DOI：10.1016/j.ifacol.2015.10.187
语种：English
出版社：Elsevier
摘要：AbstractThe 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).
关键词：Key WordsMachine learningBiological and medical system modellingSystem identification and validation