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Relational Learning for Sustainable Health

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Computational Sustainability

Part of the book series: Studies in Computational Intelligence ((SCI,volume 645))

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Abstract

Sustainable healthcare is a global need and requires better value–better health–for patients at lower cost. Predictive models have the opportunity to greatly increase value without increasing cost. Concrete examples include reducing heart attacks and reducing adverse drug events by accurately predicting them before they occur. In this paper we examine how accurately such events can be predicted presently and discuss a machine learning approach that produces accurate such predictive models.

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Notes

  1. 1.

    http://www.alz.org/alzheimers_disease_trajectory.asp.

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Author information

Authors and Affiliations

  1. School of Informatics and Computing, Indiana University, Bloomington, USA

    Sriraam Natarajan

  2. Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, USA

    Peggy L. Peissig

  3. Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, USA

    David Page

Authors
  1. Sriraam Natarajan
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  2. Peggy L. Peissig
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  3. David Page
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Corresponding author

Correspondence to Sriraam Natarajan .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Applied Sciences Zittau/Gö, Goerlitz, Germany

    Jörg Lässig

  2. Fraunhofer IAIS, University of Bonn, Sankt Augustin, Germany

    Kristian Kersting

  3. Department of Computer Science, TU Dortmund University, Dortmund, Germany

    Katharina Morik

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Natarajan, S., Peissig, P.L., Page, D. (2016). Relational Learning for Sustainable Health. In: Lässig, J., Kersting, K., Morik, K. (eds) Computational Sustainability. Studies in Computational Intelligence, vol 645. Springer, Cham. https://doi.org/10.1007/978-3-319-31858-5_11

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  • DOI: https://doi.org/10.1007/978-3-319-31858-5_11

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  • Online ISBN: 978-3-319-31858-5

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