Pregled bibliografske jedinice broj: 1054346
Visual analytics for concept exploration in subspaces of patient groups
Visual analytics for concept exploration in subspaces of patient groups // Brain informatics, 3 (2016), 4; 233-247 doi:10.1007/s40708-016-0043-5 (međunarodna recenzija, članak, znanstveni)
CROSBI ID: 1054346 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Visual analytics for concept exploration in
subspaces of patient groups
Autori
Hund, Michael ; Böhm, Dominic ; Sturm, Werner ; Sedlmair, Michael ; Schreck, Tobias ; Ullrich, Torsten ; Keim, Daniel A. ; Majnaric, Ljiljana ; Holzinger, Andreas
Izvornik
Brain informatics (2198-4018) 3
(2016), 4;
233-247
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
Knowledge discovery and exploration Visual analytics Subspace clustering Subspace analysis Subspace exploration and comparison
Sažetak
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 highdimensional 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.
Izvorni jezik
Engleski
Citiraj ovu publikaciju:
Časopis indeksira:
- Scopus
