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Knowledge Discovery and Data Mining in Biomedical Informatics: The Future Is in Integrative, Interactive Machine Learning Solutions

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Interactive Knowledge Discovery and Data Mining in Biomedical Informatics

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8401))

Abstract

Biomedical research is drowning in data, yet starving for knowledge. Current challenges in biomedical research and clinical practice include information overload – the need to combine vast amounts of structured, semi-structured, weakly structured data and vast amounts of unstructured information – and the need to optimize workflows, processes and guidelines, to increase capacity while reducing costs and improving efficiencies. In this paper we provide a very short overview on interactive and integrative solutions for knowledge discovery and data mining. In particular, we emphasize the benefits of including the end user into the “interactive” knowledge discovery process. We describe some of the most important challenges, including the need to develop and apply novel methods, algorithms and tools for the integration, fusion, pre-processing, mapping, analysis and interpretation of complex biomedical data with the aim to identify testable hypotheses, and build realistic models. The HCI-KDD approach, which is a synergistic combination of methodologies and approaches of two areas, Human–Computer Interaction (HCI) and Knowledge Discovery & Data Mining (KDD), offer ideal conditions towards solving these challenges: with the goal of supporting human intelligence with machine intelligence. There is an urgent need for integrative and interactive machine learning solutions, because no medical doctor or biomedical researcher can keep pace today with the increasingly large and complex data sets – often called “Big Data”.

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Authors and Affiliations

  1. Institute for Medical Informatics, Statistics and Documentation, Research Unit HCI, Austrian IBM Watson Think Group, Medical University Graz, Auenbruggerplatz 2/V, A-8036, Graz, Austria

    Andreas Holzinger

  2. IBM Life Sciences Discovery Centre, and TECHNA Institute for the Advancement of Technology for Health, Princess Margaret Cancer Centre, University Health Network, TMDT 11-314, 101 College Street, Toronto, ON, M5G 1L7, Canada

    Igor Jurisica

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  1. Research Unit Human-Computer Interaction, Austrian IBM Watson Think Gruop, Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Auenbruggerplatz 2/V, 8036, Graz, Austria

    Andreas Holzinger

  2. IBM Life Sciences Discovery Centre, TECHNA for the Advancement of Technology for Health, Princess Margaret Cancer Centre, University Health Network, TMDT Room 11-314, 101 College Street, M5G 1L7, Toronto, ON, Canada

    Igor Jurisica

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Holzinger, A., Jurisica, I. (2014). Knowledge Discovery and Data Mining in Biomedical Informatics: The Future Is in Integrative, Interactive Machine Learning Solutions. In: Holzinger, A., Jurisica, I. (eds) Interactive Knowledge Discovery and Data Mining in Biomedical Informatics. Lecture Notes in Computer Science, vol 8401. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43968-5_1

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