Meta-analysis of disjoint sets of attributes in large cohort studies

Jonathan K. Vis, Joost N. Kok

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)

Abstract

We will introduce the problem of classification in large cohort studies containing heterogeneous data. The data in a cohort study comes in separate groups, which can be turned on or off. Each group consists of data coming from one specific measurement instrument. We provide a “cross-sectional” investigation on this data to see the relative power of the different groups. We also propose a way of improving on the classification performance in individual cohort studies using other cohort studies by using an intuitive workflow approach.

Original languageEnglish
Title of host publicationLeveraging Applications of Formal Methods, Verification and Validation - SpecializedTechniques andApplications - 6th International Symposium, ISoLA 2014, Proceedings
EditorsTiziana Margaria, Tiziana Margaria, Bernhard Steffen
PublisherSpringer
Pages407-419
Number of pages13
ISBN (Electronic)9783662452301
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event6th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation, ISoLA 2014 - Imperial, Corfu, Greece
Duration: 8 Oct 201411 Oct 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8803
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation, ISoLA 2014
Country/TerritoryGreece
CityImperial, Corfu
Period8/10/1411/10/14

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2014.

Keywords

  • Classification
  • Cohort studies
  • Data mining
  • Feature selection
  • Machine learning
  • Meta-analysis

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