20th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems

Holzinger, A. (Speaker)

Institute of Interactive Systems and Data Science (7060)

Activity: Talk or presentation › Invited talk › Science to science

Period	6 Sept 2016
Event title	20th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems
Event type	Conference
Location	York, United KingdomShow on map

Keywords

Machine Learning
Health Informatics
interactive Machine Learning (iML)

ASJC Scopus subject areas

Artificial Intelligence

Fields of Expertise

Information, Communication & Computing

Treatment code (Nähere Zuordnung)

Basic - Fundamental (Grundlagenforschung)
Experimental

Documents & Links

The goal of ML is to develop algorithms which can learn and improve over time and in automatic machine learning (aML) great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from big data with many training sets. In the biomedical domain, we are often confronted with a small number of data sets, where aML-approaches suffer of insufficient training samples. Moreover in the medical domain we are confronted with uncertainties and non-determinism on which such algorithms can not easily be applied. Here, interactive Machine Learning (iML) may be of help, defined as "algorithms that can interact with agents and can optimize their learning behaviour through these interactions, where the agents can also be human". A "doctor-in-the-loop" can be beneficial in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization of health data, where human expertise can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise be an NP-hard problem reduces greatly in complexity through the input and the assistance of a human agent involved in the learning phase. However, for the successful application of ML in the biomedical domain a multidisciplinary skill set is required, encompassing the following seven specializations: 1) data science, 2) algorithms, 3) network science, 4) graphs/topology, 5) time/entropy, 6) data visualization, and 7) privacy, data protection, safety and security, fostered in the HCI-KDD approach

20th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems

Keywords

ASJC Scopus subject areas

Fields of Expertise

Treatment code (Nähere Zuordnung)

Documents & Links

Related content

Publications

Interactive Machine Learning for Health Informatics: When do we need the human-in-the-loop?