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2016 | OriginalPaper | Buchkapitel

Machine Learning for Health Informatics

verfasst von : Andreas Holzinger

Erschienen in: Machine Learning for Health Informatics

Verlag: Springer International Publishing

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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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Metadaten
Titel
Machine Learning for Health Informatics
verfasst von
Andreas Holzinger
Copyright-Jahr
2016
Buch
Machine Learning for Health Informatics

Print ISBN: 978-3-319-50477-3

Electronic ISBN: 978-3-319-50478-0

Copyright-Jahr: 2016

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