  Applying machine learning in the health care domain has shown promising
results in recent years. Interpretable outputs from learning algorithms are
desirable for decision making by health care personnel. In this work, we
explore the possibility of utilizing causal relationships to refine diagnostic
prediction. We focus on the task of diagnostic prediction using discomfort
drawings, and explore two ways to employ causal identification to improve the
diagnostic results. Firstly, we use causal identification to infer the causal
relationships among diagnostic labels which, by itself, provides interpretable
results to aid the decision making and training of health care personnel.
Secondly, we suggest a post-processing approach where the inferred causal
relationships are used to refine the prediction accuracy of a multi-view
probabilistic model. Experimental results show firstly that causal
identification is capable of detecting the causal relationships among
diagnostic labels correctly, and secondly that there is potential for improving
pain diagnostics prediction accuracy using the causal relationships.
