Clustering the whole-person health data to predict liver transplant survival

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

This study aims to discover groups (clusters) of patient who share whole-person characteristics. An unsupervised clustering analysis using a hierarchical agglomerative approach was applied to identify meaningful groups of patient characteristics. Results showed that is possible to identify clusters that have similar patient characteristics, and that these characteristics may be associated with survival.

Original languageEnglish (US)
Title of host publicationNursing Informatics 2016 - eHealth for All
Subtitle of host publicationEvery Level Collaboration - From Project to Realization
EditorsWalter Sermeus, Patrick Weber, Paula M. Procter
PublisherIOS Press
Pages382-386
Number of pages5
ISBN (Electronic)9781614996576
DOIs
StatePublished - Jan 1 2016
Event13th International Conference on Nursing Informatics, NI 2016 - Geneva, Switzerland
Duration: Jun 25 2016Jun 29 2016

Publication series

NameStudies in Health Technology and Informatics
Volume225
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Other

Other13th International Conference on Nursing Informatics, NI 2016
CountrySwitzerland
CityGeneva
Period6/25/166/29/16

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Keywords

  • Cluster analysis
  • Health data
  • Informatics
  • Liver transplantation
  • Predictors

Cite this

Pruinelli, L., Monsen, K. A., Simon, G. J., & Westra, B. L. (2016). Clustering the whole-person health data to predict liver transplant survival. In W. Sermeus, P. Weber, & P. M. Procter (Eds.), Nursing Informatics 2016 - eHealth for All: Every Level Collaboration - From Project to Realization (pp. 382-386). (Studies in Health Technology and Informatics; Vol. 225). IOS Press. https://doi.org/10.3233/978-1-61499-658-3-382