title: Clustering the Whole-Person Health Data to Predict Liver Transplant Survival.
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.
authors.affiliation: School of Nursing, University of Minnesota, Minneapolis, USA.
authors.forename: Lisiane
authors.initials: L
authors.lastname: Pruinelli
authors.affiliation: School of Nursing, University of Minnesota, Minneapolis, USA.
authors.forename: Karen A
authors.initials: KA
authors.lastname: Monsen
authors.affiliation: Mayo Clinic, Rochester, Minnesota, USA.
authors.forename: Gyorgy J
authors.initials: GJ
authors.lastname: Simon
authors.affiliation: School of Nursing, University of Minnesota, Minneapolis, USA.
authors.forename: Bonnie L
authors.initials: BL
authors.lastname: Westra
mesh_terms: D016000:Cluster Analysis; D005260:Female; D006801:Humans; D016031:Liver Transplantation / Q000401:mortality* / Q000706:statistics & numerical data; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D012307:Risk Factors; D016019:Survival Analysis