<abstract xmlns="http://www.w3.org/1999/xhtml"><p> A prerequisite of personalized medicine is the identification of groups of people who share specific risk factors towards an outcome. We investigate the potential of subspace clustering for finding such groups in epidemiological data. We propose a workflow that encompasses clusterability assessment before cluster discovery and quality assessment after learning the clusters. Epidemiological usually do not have a ground truth for the verification of clusters found in subspaces. Hence, we introduce quality assessment through juxtaposition of the learned models to “models-of-randomness”, i.e. models that do not reflect a true cluster structure. On the basis of this workflow, we select subspace clustering methods, compare and discuss their performance. We use a dataset with hepatic steatosis as outcome, but our findings apply on arbitrary epidemiological cohort data that have tenths of variables and exhibit class skew. </p></abstract>

A prerequisite of personalized medicine is the identification of groups of people who share specific risk factors towards an outcome. We investigate the potential of subspace clustering for finding such groups in epidemiological data. We propose a workflow that encompasses clusterability assessment before cluster discovery and quality assessment after learning the clusters. Epidemiological usually do not have a ground truth for the verification of clusters found in subspaces. Hence, we introduce quality assessment through juxtaposition of the learned models to “models-of-randomness”, i.e. models that do not reflect a true cluster structure. On the basis of this workflow, we select subspace clustering methods, compare and discuss their performance. We use a dataset with hepatic steatosis as outcome, but our findings apply on arbitrary epidemiological cohort data that have tenths of variables and exhibit class skew.

Subpopulation Discovery in Epidemiological Data with Subspace Clustering

Otto-von-Guericke University Magdeburg, Germany

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Subpopulation Discovery in Epidemiological Data with Subspace Clustering"}

A prerequisite of personalized medicine is the identification of groups of people who share specific risk factors towards an outcome. We investigate the...

Subpopulation Discovery in Epidemiological Data with Subspace Clustering

Data publikacji: 20 gru 2014

Zakres stron: 271 - 300

Otrzymano: 01 sie 2014

DOI: https://doi.org/10.2478/fcds-2014-0015

© by Uli Niemann

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.