Ontology-Guided Principal Component Analysis: Reaching the limits of the doctor-in-the-loop

Sandra Wartner, Dominic Girardi, Johannes Trenkler, Manuela Wiesinger-Widi, Raimund Kleiser, Andreas Holzinger

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Biomedical research requires deep domain expertise to perform analyses of complex data sets, assisted by mathematical expertise
provided by data scientists who design and develop sophisticated methods and tools. Such methods and tools not only require preprocessing
of the data, but most of all a meaningful input selection. Usually, data
scientists do not have sufficient background knowledge about the origin
of the data and the biomedical problems to be solved, consequently a
doctor-in-the-loop can be of great help here. In this paper we revise the
viability of integrating an analysis guided visualization component in an
ontology-guided data infrastructure, exemplified by the principal component analysis. We evaluated this approach by examining the potential for
intelligent support of medical experts on the case of cerebral aneurysms
research.
Original languageEnglish
Title of host publicationSpringer Lecture Notes in Computer Science LNCS 9832
Subtitle of host publicationInformation Technology in Bio- and Medical Informatics
Place of PublicationHeidelberg, Berlin, New York
PublisherSpringer
Pages22-33
DOIs
Publication statusPublished - 19 Aug 2016

Keywords

  • health informatics
  • Machine Learning
  • Doctor-in-the-loop

ASJC Scopus subject areas

  • Artificial Intelligence

Fields of Expertise

  • Information, Communication & Computing

Treatment code (Nähere Zuordnung)

  • Basic - Fundamental (Grundlagenforschung)

Fingerprint Dive into the research topics of 'Ontology-Guided Principal Component Analysis: Reaching the limits of the doctor-in-the-loop'. Together they form a unique fingerprint.

Cite this