A Guide for Constructing Bayesian Network Graphs of Cancer Treatment Decisions
Mario A. Cypko, Stoehr Matthaeus, Oeltze-Jafra Steffen, A Dietz, Lemke Heinz U. · Studies in health technology and informatics · 2017
In complex cancer cases, Bayesian networks can support clinical experts in finding the best patient-specific therapeutic decisions. However, the development of decision networks requires teamwork of at least one domain expert and one knowledge engineer making the process expensive, time-consuming, and prone to misunderstandings. We present a novel method for guided modeling. This method enables domain experts to model collaboratively without the need of knowledge engineers, increasing both the development speed and model quality.