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Machine Learning for Health Informatics

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Machine Learning for Health Informatics

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9605))

Abstract

Machine Learning (ML) studies algorithms which can learn from data to gain knowledge from experience and to make decisions and predictions. Health Informatics (HI) studies the effective use of probabilistic information for decision making. The combination of both has greatest potential to rise quality, efficacy and efficiency of treatment and care. Health systems worldwide are confronted with “big data” in high dimensions, where the inclusion of a human is impossible and automatic ML (aML) show impressive results. However, sometimes we are confronted with complex data, “little data”, or rare events, where aML-approaches suffer of insufficient training samples. Here interactive ML (iML) may be of help, particularly with a doctor-in-the-loop, e.g. in subspace clustering, k-Anonymization, protein folding and protein design. However, successful application of ML for HI needs an integrated approach, fostering a concerted effort of four areas: (1) data science, (2) algorithms (with focus on networks and topology (structure), and entropy (time), (3) data visualization, and last but not least (4) privacy, data protection, safety & security.

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Acknowledgments

I am very grateful for fruitful discussions with members of the HCI-KDD network and I thank my Institutes both at Graz University of Technology and the Medical University of Graz, my colleagues and my students for the enjoyable academic freedom, the inspiring intellectual environment, and the opportunity to follow my personal motto: Science is to test crazy ideas - Engineering is to put these ideas into Business. Last but not least, I thank all students of my course LV 185.A83 (http://hci-kdd.org/machine-learning-for-health-informatics-course), at Vienna University of Technology for their kind interest and motivating feedback.

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  1. Holzinger Group, HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Graz, Austria

    Andreas Holzinger

  2. Institute for Information Systems and Computer Media, Graz University of Technology, Graz, Austria

    Andreas Holzinger

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    Andreas Holzinger

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Holzinger, A. (2016). Machine Learning for Health Informatics. In: Holzinger, A. (eds) Machine Learning for Health Informatics. Lecture Notes in Computer Science(), vol 9605. Springer, Cham. https://doi.org/10.1007/978-3-319-50478-0_1

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  • DOI: https://doi.org/10.1007/978-3-319-50478-0_1

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