Structural Positional Encoding for knowledge integration in
  transformer-based medical process monitoring
Christopher Irwin Marco Dossena Giorgio Leonardi Stefania Montani
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.
Decision support and quality assessment in medicine cannot ignore domain
knowledge, in order to be grounded on all the available information (which is
not limited to data) and to be really acceptable by end users.
  In this paper, we propose a predictive process monitoring approach relying on
the use of a {\em transformer}, a deep learning architecture based on the
attention mechanism. 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 presents and discusses the encouraging
experimental result we are collecting in the domain of stroke management.