Similarity measuring between patient traces for clinical pathway analysis

Z. Huang, X. Lu, H. Duan

Research output: Chapter in Book/Report/Conference proceedingChapterAcademic

6 Citations (Scopus)
125 Downloads (Pure)

Abstract

Clinical pathways leave traces, described as activity sequences with regard to a mixture of various latent treatment behaviors. Measuring similarities between patient traces can profitably be exploited further as a basis for providing insights into the pathways, and complementing existing techniques of clinical pathway analysis, which mainly focus on looking at aggregated data seen from an external perspective. In this paper, a probabilistic graphical model, i.e., Latent Dirichlet Allocation, is employed to discover latent treatment behaviors of patient traces for clinical pathways such that similarities of pairwise patient traces can be measured based on their underlying behavioral topical features. The presented method, as a basis for further tasks in clinical pathway analysis, are evaluated via a real-world data-set collected from a Chinese hospital.
Original languageEnglish
Title of host publicationProceedings of the 14th Conference on Artificial Intelligence in Medicine, AIME2013, 29 May - 1 June 2013, Murcia, Spain
EditorsN. Peek, R. Marin Morales, M. Peleg
Place of PublicationBerlin
PublisherSpringer
Pages268-272
ISBN (Print)978-3-642-38326-7
DOIs
Publication statusPublished - 2013
Eventconference; AIME2013 -
Duration: 1 Jan 2013 → …

Publication series

NameLecture Notes in Computer Science
Volume7885

Conference

Conferenceconference; AIME2013
Period1/01/13 → …
OtherAIME2013

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