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