Subpopulation Discovery and Validation in Epidemiological Data

Motivated by identifying subpopulations that share common characteristics (e.g. alcohol consumption) to explain risk factors of diseases in cohort study data, we used subspace clustering to discover such subpopulations. In this paper, we describe our interactive coordinated multiple view system Visual Analytics framework S-ADVIsED for SubpopulAtion Discovery and Validation In Epidemiological Data. S-ADVIsED enables epidemiologists to explore and validate findings derived from subspace clustering. We investigated the replication of a selected subpopulation in an independent population.

[1]  Mark Woodward,et al.  Epidemiology: Study Design and Data Analysis , 1999 .

[2]  Josua Krause,et al.  Supporting Iterative Cohort Construction with Visual Temporal Queries , 2016, IEEE Transactions on Visualization and Computer Graphics.

[3]  Mary K Obenshain.  Application of Data Mining Techniques to Healthcare Data , 2004, Infection Control & Hospital Epidemiology.

[4]  Daniel A. Keim,et al.  ClustNails: Visual Analysis of Subspace Clusters , 2012 .

[5]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[6]  Kai Lawonn,et al.  3D Regression Heat Map Analysis of Population Study Data , 2016, IEEE Transactions on Visualization and Computer Graphics.

[7]  Bernhard Preim,et al.  Visual Analytics of Image-Centric Cohort Studies in Epidemiology , 2015, Visualization in Medicine and Life Sciences III.

[8]  Daniel A. Keim,et al.  Visual analytics for concept exploration in subspaces of patient groups , 2016, Brain Informatics.

[9]  Ira Assent,et al.  VISA: visual subspace clustering analysis , 2007, SKDD.

[10]  Kai Lawonn,et al.  Interactive Visual Analysis of Image-Centric Cohort Study Data , 2014, IEEE Transactions on Visualization and Computer Graphics.

[11]  H. Völzke,et al.  Study of Health in Pomerania (SHIP) , 2012, Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz.

[12]  Daniel A. Keim,et al.  Visual Quality Assessment of Subspace Clusterings , 2016 .

[13]  Myra Spiliopoulou,et al.  Identifying Relevant Features for a Multi-factorial Disorder with Constraint-Based Subspace Clustering , 2016, 2016 IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS).

[14]  Zhiyuan Zhang,et al.  Iterative cohort analysis and exploration , 2015, Inf. Vis..

[15]  Huan Liu,et al.  Subspace clustering for high dimensional data: a review , 2004, SKDD.

[16]  Jeffrey Heer,et al.  D³ Data-Driven Documents , 2011, IEEE Transactions on Visualization and Computer Graphics.

[17]  Myra Spiliopoulou,et al.  Can We Classify the Participants of a Longitudinal Epidemiological Study from Their Previous Evolution? , 2015, 2015 IEEE 28th International Symposium on Computer-Based Medical Systems.