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Using Machine Learning to Predict Length of Stay and Discharge Destination for Hip-Fracture Patients

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Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016 (IntelliSys 2016)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 15))

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Abstract

Faced with the challenge of population ageing, healthcare providers are increasingly in need for evidence-based artefacts to support the decision making process. In this regard, the paper avails of machine learning techniques in a bid to support the elderly care planning with application to hip fracture care in Ireland. Specifically, the inpatient length of stay (LOS), and discharge destination are aimed to be predicted based on learning from patient historical data. The accuracy of various regression and classification techniques was investigated. Random Forests proved to provide a considerable higher accuracy in comparison to other algorithms in our case. The prediction models were trained using the Azure Machine Learning Studio. Furthermore, the models were published as predictive web services on top of the Azure cloud platform. The developed predictors are claimed to make predictions on the LOS and discharge destinations with high accuracy.

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Authors and Affiliations

  1. National University of Ireland Galway, Galway, Ireland

    Mahmoud Elbattah & Owen Molloy

Authors
  1. Mahmoud Elbattah
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  2. Owen Molloy
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Correspondence to Mahmoud Elbattah .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Engineering, School of Computing and Mathematics, University of Ulster at Jordanstown, Newtownabbey, United Kingdom

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Rahul Bhatia

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Elbattah, M., Molloy, O. (2018). Using Machine Learning to Predict Length of Stay and Discharge Destination for Hip-Fracture Patients. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. IntelliSys 2016. Lecture Notes in Networks and Systems, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-319-56994-9_15

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  • DOI: https://doi.org/10.1007/978-3-319-56994-9_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56993-2

  • Online ISBN: 978-3-319-56994-9

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