Clustering clinical and health care processes using a novel measure of dissimilarity for variable-length sequences of ordinal states
Hannah Johns, John Hearne, Julie Bernhardt, Leonid Churilov
STATISTICAL METHODS IN MEDICAL RESEARCH | SAGE PUBLICATIONS LTD | Published : 2020
Clinical and health care processes are often summarised through sequences of ordinal data describing patient's state over time. Identifying patterns in these sequences can provide valuable insights into patient progression trajectories for the purposes of clinical monitoring and quality assurance. However, both the variation in the length of each sequence and the ordinal nature of observable states present challenges to pattern identification. In this paper, we address these challenges by presenting a novel measure of dissimilarity for comparing two or more variable-length ordinal sequences that can be used in conjunction with conventional clustering methods to identify patterns in patient p..View full abstract
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The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: HJ would like to acknowledge the support provided by an Australian Government Research Training Program Scholarship. HJ, JB and LC would like to acknowledge the support provided by the NHMRC Centre of Research Excellence in Stroke Rehabilitation and Brain Recovery.