Predictive process monitoring is a process mining task aimed at forecasting information about a running process trace, such as the most correct next activity to be executed. In medical domains, predictive process monitoring can provide valuable decision support in atypical and nontrivial situations. In this paper, we propose a predictive process monitoring approach relying on the use of a Transformer, a deep learning architecture based on the attention mechanism, that we are testing in the domain of stroke management. A major contribution of our work lies in the incorporation of ontological domain-specific knowledge, carried out through a graph positional encoding technique. The paper also presents and discusses the first encouraging experimental result we are collecting.