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Prof. Andreas Holzinger

Medical University Graz, Austria

Machine Learning for health informatics: recent trends and applications


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.

Andreas Holzinger


Andreas and his Group work on extracting knowledge from data and foster a synergistic combination of methodologies of two areas that offer ideal conditions towards unraveling problems with complex health data: Human-Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), with the central goal of supporting human intelligence with machine learning to discover novel, previously unknown insights into data. Andreas is founder and leader of the international Expert Network HCI-KDD, Assoc. Editor of Knowledge and Information Systems (KAIS), Brain Informatics (BRIN), and member of IFIP WG 12.9 Computational Intelligence. Andreas is head of the Research Unit HCI-KDD, Institute for Medical Informatics, Statistics and Documentation at the Medical University Graz, Associate Professor at Graz University of Technology, where he teaches Biomedical Informatics, and supervises engineering students at the Institute of Information Systems and he is currently Visiting Professor at Vienna University of Technology, where he is teaching machine learning for health informatics at the Faculty of Informatics. Andreas holds a PhD (1998) in Cognitive Science from Graz University and a Habilitation (second PhD, 2003) in Computer Science from Graz University of Technology. Andreas was Visiting Professor in Berlin, Innsbruck, London (2 times), Aachen and Vienna.

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20th Annual
KES Conference
York, UK
5, 6 & 7 Sept 2016