Personalization of Infectious Disease Risk Prediction: Towards Automatic Generation of a Bayesian Network
Infectious diseases have been a major cause of human morbidity, but most are avoidable. A relevant and accurate risk prediction is expected to alert people to the risk of getting exposed to infectious diseases. However, current approaches are limited to the contexts and static risk prediction model. Thus, a dynamic and growing prediction model, based on Bayesian Network (BN), is proposed to overcome these limitations. The objectives of this article are (1) to describe the methodology of generating the dynamic BN from a rule-based model, and (2) to examine the accuracy of the prediction result. The importance of this paper is that it provides the design of computer procedures that aim to acquire knowledge from domain expert to generate Dynamic BN structure and populate its Conditional Probability Tables.