A new formalism for temporal modeling in medical decision-support systems

We present a new mathematical formalism, which we call modifiable temporal belief networks (MTBNs) that extends the concept of an ordinary belief network (BN) to incorporate a dynamic causal structure and explicit temporal semantics. An important feature of MTBNs is that they allow portions of the model to be abstract and portions of it to be temporally explicit. We show how this property can lead to substantial knowledge acquisition and computational complexity savings. In addition to temporal modeling, the language of MTBNs can be an important analytical tool, as well as temporal language for causal discovery.