Exploiting causal functional relationships in Bayesian network modelling for personalised healthcare

• Physiological knowledge can guide the modelling of personalised decision–support systems.
• We propose a general methodology for building practical probabilistic clinical models.
• We propose criteria for evaluating the capabilities of the models built.
• The methodology is straightforward used to build a model for preeclampsia development
• The model shows good diagnostic and prognostic capabilities, and practical suitability.

Bridging the gap between the theory of Bayesian networks and solving an actual problem is still a big challenge and this is in particular true for medical problems, where such a gap is clearly evident. We argue that Bayesian networks offer appropriate technology for the successful modelling of medical problems, including the personalisation of healthcare. Personalisation is an important aspect of remote disease management systems. It involves the forecasting of progression of a disease based on the interpretation of patient data by a disease model. A natural foundation for disease models is physiological knowledge, as such knowledge facilitates building clinically understandable models. This paper proposes ways to represent such knowledge as part of engineering principles employed in building clinically practical probabilistic models. The methodology has been used to construct a temporal Bayesian network model for preeclampsia – a pregnancy-related disorder. The model is the first of its kind and an integral part of a mobile home-monitoring system intended for use in daily pregnancy care. We conducted an evaluation study with actual patient data to obtain insight into the model’s performance and suitability. The results obtained are encouraging and show the potential of exploiting physiological knowledge for personalised decision-support systems.