This talk presents an overview of probabilistic graphical modelling as a strategy for understanding heterogeneous subgroups of patients. The identification of such subgroups may elucidate underlying causal mechanisms which may lead to more targeted treatment and intervention strategies. We will look at (1) the ideal of personalisation within the context of machine learning for healthcare (2) “From the ideal to the reality” and (3) some of the possible pathways to progress for making the ideal of personalised healthcare to reality. The last part of this talk focuses on the pipeline of personalisation and looks at probabilistic graphical models are part of a pipeline.