Machine Learning for Health Informatics

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


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.
Original languageEnglish
Title of host publicationMachine Learning for Health Informatics: State-of-the-Art and Future Challenges, Lecture Notes in Artificial Intelligence LNAI 9605
EditorsAndreas Holzinger
Place of PublicationCham
PublisherSpringer International
ISBN (Print)978-3-319-50477-3
Publication statusPublished - 22 Dec 2016

Publication series

NameLecture Notes in Computer Science
VolumeLNAI 9605


  • Machine Learning
  • health informatics

ASJC Scopus subject areas

  • Artificial Intelligence

Fields of Expertise

  • Information, Communication & Computing

Treatment code (Nähere Zuordnung)

  • Basic - Fundamental (Grundlagenforschung)
  • Application


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