Clustering of timed sequences -- Application to the analysis of care pathways

arXiv:2404.15379v1 Announce Type: new
Abstract: Improving the future of healthcare starts by better understanding the current actual practices in hospitals. This motivates the objective of discovering typical care pathways from patient data. Revealing homogeneous groups of care pathways can be achieved through clustering. The difficulty in clustering care pathways, represented by sequences of timestamped events, lies in defining a semantically appropriate metric and clustering algorithms.
In this article, we adapt two methods developed for time series to time sequences: the …

abstract analysis application arxiv clustering cs.ai cs.lg current data future healthcare hospitals improving patient practices through type understanding