Predicting future state for adaptive clinical pathway management

Clinical decision support systems are assisting physicians in providing care to patients. However, the context of clinical pathway management such rather limited as they only take current state patient into account and ignore possible evolvement that future. In past decade, availability big data healthcare domain did open a new era for support. Machine learning technologies now widely used domain, nevertheless, mostly tool disease prediction. A not predicts future states, but also enables adaptive based on these predictions is still need. This paper introduces weighted transition logic, logic model changes actions planned pathways. Weighted extends linear by taking weights -- numerical values indicating quality an action or entire account. It allows us predict states it predictions. We provide implementation using semantic web technologies, which makes easy integrate rules background knowledge. Executed reasoner, generate towards target state, well detect potential conflicts when multiple pathways coexisting. The transitions from predicted traceable, builds trust human users generated pathway.