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Machine Learning for Early DRG Classification

In: Optimizing Hospital-wide Patient Scheduling

Author

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  • Daniel Gartner

    (Technische Universität München)

Abstract

In this chapter, a literature review of machine learning methods is provided with a special focus on attribute selection and classification methods successfully employed in health care. Similarities and differences between the machine learning methods addressed in this dissertation and the approaches available from the literature are highlighted. Afterwards, techniques for selecting relevant and non-redundant attributes for early DRG classification are presented. Finally, different classification techniques are described in detail.

Suggested Citation

  • Daniel Gartner, 2014. "Machine Learning for Early DRG Classification," Lecture Notes in Economics and Mathematical Systems, in: Optimizing Hospital-wide Patient Scheduling, edition 127, chapter 0, pages 9-31, Springer.
    • Handle: RePEc:spr:lnechp:978-3-319-04066-0_2
    DOI: 10.1007/978-3-319-04066-0_2
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    Cited by:

    1. Siyu Zeng & Li luo & Fang Chen & Yue Li & Mei Chen & Xiaozhou He, 2021. "Association of outdoor air pollution with the medical expense of ischemic stroke: The case study of an industrial city in western China," International Journal of Health Planning and Management, Wiley Blackwell, vol. 36(3), pages 715-728, May.

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