In contrast to traditional, industrial applications such as market basket analysis, the process of knowledge discovery in medical research is mostly performed by the medical domain experts themselves. This is mostly due to the high complexity of the research domain, which requires deep domain knowledge. At the same time, these domain experts face major obstacles in handling and analyzing their high-dimensional, heterogeneous, and complex research data. In this paper, we present a generic, ontology-centered data infrastructure for scientific research which actively supports the medical domain experts in data acquisition, processing and exploration. We focus on the system’s capabilities to automatically perform dimensionality reduction algorithms on arbitrary high-dimensional data sets and allow the domain experts to visually explore their high-dimensional data of interest, without needing expert IT or specialized database knowledge.
|Title of host publication||Advanced Data Analytics in Health|
|Editors||Philippe J. Giabbanelli, Vijay K Mago, Elpiniki I. Papageorgiou|
|Place of Publication||Cham|
|Publication status||Published - 21 Apr 2018|
- Dimensionality Reduction
- Knowledge Discovery
- Data Science
- Information Science
Girardi, D., & Holzinger, A. (2018). Dimensionality Reduction for Exploratory Data Analysis in Daily Medical Research. In P. J. Giabbanelli, V. K. Mago, & E. I. Papageorgiou (Eds.), Advanced Data Analytics in Health (pp. 3-20). Cham: Springer International.