Visual analytics for concept exploration in subspaces of patient groups : Making sense of complex datasets with the Doctor-in-the-loop

Blumenschein, Michael; Böhm, Dominic; Sturm, Werner; Sedlmair, Michael; Schreck, Tobias; Ullrich, Torsten; Keim, Daniel A.; Majnaric, Ljiljana; Holzinger, Andreas

Visual analytics for concept exploration in subspaces of patient groups : Making sense of complex datasets with the Doctor-in-the-loop

Dateien

Hund_0-345369.pdfGröße: 1.84 MBDownloads: 486

Datum

2016

Autor:innen

Blumenschein, Michael

Böhm, Dominic

Sturm, Werner

Sedlmair, Michael

Schreck, Tobias

Ullrich, Torsten

Keim, Daniel A.

Majnaric, Ljiljana

Holzinger, Andreas

Open Access-Veröffentlichung

Open Access Hybrid

Sammlungen

Informatik und Informationswissenschaft

Publikationstyp

Zeitschriftenartikel

Publikationsstatus

Published

Erschienen in

Brain Informatics. 2016, 3(4), pp. 233-247. ISSN 2198-4018. eISSN 2198-4026. Available under: doi: 10.1007/s40708-016-0043-5

Zusammenfassung

Medical doctors and researchers in bio-medicine are increasingly confronted with complex patient data, posing new and difficult analysis challenges. These data are often comprising high-dimensional descriptions of patient conditions and measurements on the success of certain therapies. An important analysis question in such data is to compare and correlate patient conditions and therapy results along with combinations of dimensions. As the number of dimensions is often very large, one needs to map them to a smaller number of relevant dimensions to be more amenable for expert analysis. This is because irrelevant, redundant, and conflicting dimensions can negatively affect effectiveness and efficiency of the analytic process (the so-called curse of dimensionality). However, the possible mappings from high- to low-dimensional spaces are ambiguous. For example, the similarity between patients may change by considering different combinations of relevant dimensions (subspaces). We demonstrate the potential of subspace analysis for the interpretation of high-dimensional medical data. Specifically, we present SubVIS, an interactive tool to visually explore subspace clusters from different perspectives, introduce a novel analysis workflow, and discuss future directions for high-dimensional (medical) data analysis and its visual exploration. We apply the presented workflow to a real-world dataset from the medical domain and show its usefulness with a domain expert evaluation.

Fachgebiet (DDC)

004 Informatik

Zitieren

ISO 690

BLUMENSCHEIN, Michael, Dominic BÖHM, Werner STURM, Michael SEDLMAIR, Tobias SCHRECK, Torsten ULLRICH, Daniel A. KEIM, Ljiljana MAJNARIC, Andreas HOLZINGER, 2016. Visual analytics for concept exploration in subspaces of patient groups : Making sense of complex datasets with the Doctor-in-the-loop. In: Brain Informatics. 2016, 3(4), pp. 233-247. ISSN 2198-4018. eISSN 2198-4026. Available under: doi: 10.1007/s40708-016-0043-5

BibTex

@article{Blumenschein2016-12Visua-34862,
  year={2016},
  doi={10.1007/s40708-016-0043-5},
  title={Visual analytics for concept exploration in subspaces of patient groups : Making sense of complex datasets with the Doctor-in-the-loop},
  number={4},
  volume={3},
  issn={2198-4018},
  journal={Brain Informatics},
  pages={233--247},
  author={Blumenschein, Michael and Böhm, Dominic and Sturm, Werner and Sedlmair, Michael and Schreck, Tobias and Ullrich, Torsten and Keim, Daniel A. and Majnaric, Ljiljana and Holzinger, Andreas}
}

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Universitätsbibliographie

Ja