Machine Learning for Health Informatics

Research output: Chapter in Book/Report/Conference proceedingChapter

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.
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
Pages1-24
ISBN (Print)978-3-319-50477-3
DOIs
StatusPublished - 22 Dec 2016

Publication series

NameLecture Notes in Artificial Intelligence
PublisherSpringer
Volume9605

Fingerprint

Learning systems
Health
Protein folding
Data privacy
Data visualization
Entropy
Decision making
Topology
Proteins

Keywords

  • Machine Learning
  • health informatics

ASJC Scopus subject areas

  • Artificial Intelligence

Fields of Expertise

  • Information, Communication & Computing

Treatment code (Nähere Zuordnung)

  • Basic - Fundamental (Grundlagenforschung)
  • Application

Cite this

Holzinger, A. (2016). Machine Learning for Health Informatics. In A. Holzinger (Ed.), Machine Learning for Health Informatics: State-of-the-Art and Future Challenges, Lecture Notes in Artificial Intelligence LNAI 9605 (pp. 1-24). (Lecture Notes in Artificial Intelligence; Vol. 9605). Cham: Springer International. DOI: 10.1007/978-3-319-50478-0_1

Machine Learning for Health Informatics. / Holzinger, Andreas.

Machine Learning for Health Informatics: State-of-the-Art and Future Challenges, Lecture Notes in Artificial Intelligence LNAI 9605. ed. / Andreas Holzinger. Cham : Springer International, 2016. p. 1-24 (Lecture Notes in Artificial Intelligence; Vol. 9605).

Research output: Chapter in Book/Report/Conference proceedingChapter

Holzinger, A 2016, Machine Learning for Health Informatics. in A Holzinger (ed.), Machine Learning for Health Informatics: State-of-the-Art and Future Challenges, Lecture Notes in Artificial Intelligence LNAI 9605. Lecture Notes in Artificial Intelligence, vol. 9605, Springer International, Cham, pp. 1-24. DOI: 10.1007/978-3-319-50478-0_1
Holzinger A. Machine Learning for Health Informatics. In Holzinger A, editor, Machine Learning for Health Informatics: State-of-the-Art and Future Challenges, Lecture Notes in Artificial Intelligence LNAI 9605. Cham: Springer International. 2016. p. 1-24. (Lecture Notes in Artificial Intelligence). Available from, DOI: 10.1007/978-3-319-50478-0_1
Holzinger, Andreas. / Machine Learning for Health Informatics. Machine Learning for Health Informatics: State-of-the-Art and Future Challenges, Lecture Notes in Artificial Intelligence LNAI 9605. editor / Andreas Holzinger. Cham : Springer International, 2016. pp. 1-24 (Lecture Notes in Artificial Intelligence).
@inbook{bdc222171c944a20a583ca1fb9cc9ea9,
title = "Machine Learning for Health Informatics",
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.",
keywords = "Machine Learning, health informatics",
author = "Andreas Holzinger",
year = "2016",
month = "12",
day = "22",
doi = "10.1007/978-3-319-50478-0_1",
language = "English",
isbn = "978-3-319-50477-3",
series = "Lecture Notes in Artificial Intelligence",
publisher = "Springer International",
pages = "1--24",
editor = "Andreas Holzinger",
booktitle = "Machine Learning for Health Informatics: State-of-the-Art and Future Challenges, Lecture Notes in Artificial Intelligence LNAI 9605",

}

TY - CHAP

T1 - Machine Learning for Health Informatics

AU - Holzinger,Andreas

PY - 2016/12/22

Y1 - 2016/12/22

N2 - 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.

AB - 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.

KW - Machine Learning

KW - health informatics

U2 - 10.1007/978-3-319-50478-0_1

DO - 10.1007/978-3-319-50478-0_1

M3 - Chapter

SN - 978-3-319-50477-3

T3 - Lecture Notes in Artificial Intelligence

SP - 1

EP - 24

BT - Machine Learning for Health Informatics: State-of-the-Art and Future Challenges, Lecture Notes in Artificial Intelligence LNAI 9605

PB - Springer International

CY - Cham

ER -