Interactive knowledge discovery with the doctor-in-the-loop: a practical example of cerebral aneurysms research

By Dominic Girardi, Josef Küng, Raimund Kleiser, Michael Sonnberger, Doris Csillag, Johannes Trenkler, Andreas Holzinger. 2016

36 citations recorded by the source corpus.

Established process models for knowledge discovery find the domain-expert in a customer-like and supervising role. In the field of biomedical research, it is necessary to move the domain-experts into the center of this process with far-reaching consequences for both their research output and the process itself. In this paper, we revise the established process models for knowledge discovery and propose a new process model for domain-expert-driven interactive knowledge discovery. Furthermore, we present a research infrastructure which is adapted to this new process model and demonstrate how the domain-expert can be deeply integrated even into the highly complex data-mining process and data-exploration tasks. We evaluated this approach in the medical domain for the case of cerebral aneurysms research.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Constantinos S. Pattichis
2. Douglas B. Kell
3. Chris Biemann
4. Andreas Holzinger
5. Jens Christian Bjerring
6. Jacob Busch
7. Heimo Müller
8. Kurt Zatloukal

In Computational Theory and Mathematics

1. Douglas B. Kell
2. Constantinos S. Pattichis
3. Andreas Holzinger
4. Chris Biemann

In Information Systems

1. Douglas B. Kell
2. Andreas Holzinger
3. Chris Biemann

Citations in the corpus

• What do we need to build explainable AI systems for the medical domain? (2017, background): “…reasoning, nowadays based on deep learning rather than logic-based formal reasoning (Hohenecker and Lukasiewicz, 2017) is of great interest, and the applications of ontology-guided approaches are of much practical help for the medical domain expert (Girardi et al., 2016; Wartner et al., 2016).”
• Ontology-Guided Principal Component Analysis: Reaching the Limits of the Doctor-in-the-Loop (2016, methodology): “The immediate integration of the domain expert into data exploration has already proved to be very effective, for example in knowledge discovery [9], or in subspace clustering [17], compelling the domain expert to face the major challenge of detecting mutual influences of variables.”