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Predictive Analytics and Deep Learning Techniques in Electronic Medical Records: Recent Advancements and Future Direction

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Web, Artificial Intelligence and Network Applications (WAINA 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 927))

Abstract

The demands on medical services are increasing rapidly in the global context. Therefore, handling beds availability, identifying and managing the length of stay (LOS) is creating persistent needs for the physicians, nurses, clinicians, hospital management, and caregivers in the public hospital admissions and the private hospital admissions. Health analytics provides unprecedented ways to predict trends, patients’ future outcomes, knowledge discovery, and improving the decision making in the clinical settings. This paper reviews the state-of-the-art machine learning, deep learning techniques and the related work in relation to the length of stay common hospital admissions. Research trends and future direction for the forecasting LOS in medical admissions are discussed in this paper.

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Author information

Authors and Affiliations

  1. Western Sydney University, Parramatta, Sydney, NSW, Australia

    Belal Alsinglawi & Omar Mubin

Authors
  1. Belal Alsinglawi
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  2. Omar Mubin
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Corresponding author

Correspondence to Belal Alsinglawi .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Advanced Sciences, Hosei University, Koganei-Shi, Tokyo, Japan

    Makoto Takizawa

  3. Department of Computer Science, Technical University of Catalonia, Barcelona, Barcelona, Spain

    Fatos Xhafa

  4. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

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Alsinglawi, B., Mubin, O. (2019). Predictive Analytics and Deep Learning Techniques in Electronic Medical Records: Recent Advancements and Future Direction. In: Barolli, L., Takizawa, M., Xhafa, F., Enokido, T. (eds) Web, Artificial Intelligence and Network Applications. WAINA 2019. Advances in Intelligent Systems and Computing, vol 927. Springer, Cham. https://doi.org/10.1007/978-3-030-15035-8_89

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