Explainable process trace classification: An application to stroke

Medical process trace classification exploits the activity sequences logged by an healthcare organization to classify traces themselves on the basis of some performance properties; this information can be used for quality assessment. State-of-the-art process trace classification resorts to deep learning, a very powerful technique which however suffers from the lack of explainability. In this paper we aim at addressing this issue, motivated by a relevant application, i.e., the classification of process traces for quality assessment in stroke management. To this end we introduce the novel concept of trace saliency maps, an instrument able to highlight what trace activities are particularly significant for the classification task. Through trace saliency maps we justify the output of the deep learning architecture, and make it more easily interpretable to medical users. The good results in our use case have shown the feasibility of the approach, and let us make the hypothesis that it might be translated to other application settings and to other black box learners as well.