Abstract—Process mining is a data analytics approach which has shown promising results in healthcare including the potential to improve the management of chronic diseases such as cardiovascular disease (CVD). CVD are one of the leading causes of premature death worldwide. Helping healthcare professionals develop a better understanding of how to improve CVD care pathways may result in better outcomes for patients. This paper describes the opportunities for process mining to support such improvements and provides a summary of process mining studies undertaken in the field of cardiology. The initial search found 1,597 potentially relevant papers which, after a systematic review, identified a final set of 32 relevant studies. These studies were analyzed across five themes: process and data types; research questions; techniques, perspective and tools; methodologies; limitations and suggested future work.

Index Terms—Cardiology, cardiovascular disease, care pathway, process mining.

Guntur P. Kusuma is with School of Computing, Faculty Engineering, University of Leeds, United Kingdom. He is also with School of Applied Science, Telkom University, Bandung, Indonesia (email: scgpk@leeds.ac.uk).
Marlous Hall, Chris P. Gale are with Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, United Kingdom.
Owen A. Johnson is with School of Computing, Faculty Engineering, University of Leeds, United Kingdom.

Cite: Guntur P. Kusuma, Marlous Hall, Chris P. Gale, Owen A. Johnson, "Process Mining in Cardiology: A Literature Review," International Journal of Bioscience, Biochemistry and Bioinformatics vol. 8, no. 4, pp. 226-236, 2018.