Title: Towards Knowledge Discovery with the human in the machine learning loop: An Ontology-Guided Meta-Classifying Approach for the Biomedical Domain
Lecturer: Dominic GIRADI, RISC-Software Linz, Austria <expertise>
Abstract: The process of knowledge discovery in clinical research is significantly different from other business domains, for example market research. While in the general definitions of knowledge discovery the domain expert is in a rather consulting, supervising or customer-like role, the complex process of (bio-) medical or clinical knowledge discovery requires the medical domain expert to be deeply involved into this process. At the same time, data integration and data pre-processing are known to be major pitfalls to such (bio-) medical data projects, due to the fact that in the (bio-) medical domain we are confronted with extremely high complexity, heterogeneity, along with unprecedented amounts of data sets. In this lecture it will be discussed what consequences for the knowledge discovery process arise, when the domain expert is moved to a central position of this process, and as a consequence how advanced machine learning algorithms can be combined with traditional, ontology-centered approaches for the benefit of advancing (bio-)medical research. Examples are given of different medical research projects, i.e.: clinical benchmarking, cerebral aneurysm and biometric study of children and young adults.
The theoretical focus of this talk is on how the elaborate structural meta-information of the domain ontology can be used to parametrize and automatize advanced machine learning algorithms and data visualization methods. Two examples will be presented: An ontology-guided dimensionality reduction with focus on the hierarchical structured, multi-select categorical variables and an approach of an ontology-guided meta-classifier.