A Clustering-based Sequence Variants Analysis Method for Electronic Medical Records of Multimedical Institutions

A sequence variant (SV) containing branches is considered an extension of a sequence widely used to represent an ordered list of items. Although comparing such SVs is vital in practical applications, an efficient method to compare more than three SVs efficiently has yet to be studied. When the number of SVs increases, there is a high possibility that common parts do not exist. Hence, we cannot thoughtfully understand the similarities and differences among the SVs that were compared. In this paper, we first develop a method to exclude general items that have high frequency because they appear in almost every sequence and then cluster the SVs into several groups using a defined SV similarity. Finally, we calculate the longest common SV in each group and generate a merged SV to visualize the commonality and differences of the target SVs efficiently. The proposed method is shown to be effective when applied to a real medical dataset from 23 medical institutions.