Contrary to event traces considered in traditional process mining literature, this paper addresses the problem of optimal process mining of traces of events and attributes associated with transitions. The problem is formally defined with rigorous description of the input event logs, the output process model, the event game specifying the images of traces in the model, and a non standard quality metric termed relevance for both the model and all model components. A dynamic programming algorithm is proposed to determine the optimal event game of each trace for a given process model. A multi-start local optimization algorithm built on an original concept of marginal relevance measure is developed for process model optimization. The proposed algorithm is shown to outperform benchmark algorithms on 40 generated test instances and be able to produce near optimal process model with an optimality gap of less than 4.46%. Results of this paper are also applied to a real case study of the care pathways of sarcoma patients. The event log representation is shown to be able to describe accurately the impact of the health state on the care pathways with only minor model relevance degradation. The proposed approach is shown to be able to generate process model at various precision levels and to compare the care pathways of cancer patients. It is also shown to generate better process model than the widely used process mining tools Disco and DFvM on both our relevance and the traditional fitness quality metrics. Note to Practitioners —This paper is motivated by our collaboration with the French cancer centre (Centre Léon Bérard) on data-driven modeling of sarcoma patient care pathways. The primary goal is to investigate the impact of patient health state such as cancer progression on the care pathways. We achieve this by original representation of care pathways by traces of events interleaved by health states. The original concept of “relevance” clearly measures the importance of each element in the process model. The faithfulness of the process model and its complexity can be easily controlled by precision parameters including least significance level of each model element and the number of layers of the model. A case study of Sarcoma patients is presented to show the importance of our care pathway representation, the superiority of our process mining algorithm, the difference of care pathways of four different patient management strategies, and how the health condition intervenes in different strategies.