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Leveraging Machine Learning Techniques and Engineering of Multi-Nature Features for National Daily Regional Ambulance Demand Prediction

Author

Listed:
  • Adrian Xi Lin

    (School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
    Joint first author.)

  • Andrew Fu Wah Ho

    (SingHealth Duke-NUS Emergency Medicine Academic Clinical Program, Duke-National University of Singapore Medical School, Singapore 169857, Singapore
    SingHealth Emergency Medicine Residency Programme, Duke-National University of Singapore Medical School, Singapore 169608, Singapore
    Signature Research Programme in Cardiovascular & Metabolic Disorders, Duke-National University of Singapore Medical School, Singapore 169857, Singapore
    Joint first author.)

  • Kang Hao Cheong

    (Science, Mathematics and Technology Cluster, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore
    SUTD-Massachusetts Institute of Technology International Design Centre, Singapore 487372, Singapore)

  • Zengxiang Li

    (Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore 138632, Singapore)

  • Wentong Cai

    (Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921, Singapore)

  • Marcel Lucas Chee

    (Faculty of Medicine, Nursing and Health Sciences, Monash University, VIC 3800, Australia)

  • Yih Yng Ng

    (Emergency Medicine, Tan Tock Seng Hospital, Singapore 308433, Singapore
    Home Team Medical Services Division, Ministry of Home Affairs, Singapore 179369, Singapore)

  • Xiaokui Xiao

    (School of Computing, National University of Singapore, Singapore 117417, Singapore
    Joint last author.)

  • Marcus Eng Hock Ong

    (Health Services & Systems Research, Duke-NUS Medical School, Singapore 169857, Singapore
    Department of Emergency Medicine, Singapore General Hospital, Singapore 169608, Singapore
    Joint last author.)

Abstract

The accurate prediction of ambulance demand provides great value to emergency service providers and people living within a city. It supports the rational and dynamic allocation of ambulances and hospital staffing, and ensures patients have timely access to such resources. However, this task has been challenging due to complex multi-nature dependencies and nonlinear dynamics within ambulance demand, such as spatial characteristics involving the region of the city at which the demand is estimated, short and long-term historical demands, as well as the demographics of a region. Machine learning techniques are thus useful to quantify these characteristics of ambulance demand. However, there is generally a lack of studies that use machine learning tools for a comprehensive modeling of the important demand dependencies to predict ambulance demands. In this paper, an original and novel approach that leverages machine learning tools and extraction of features based on the multi-nature insights of ambulance demands is proposed. We experimentally evaluate the performance of next-day demand prediction across several state-of-the-art machine learning techniques and ambulance demand prediction methods, using real-world ambulatory and demographical datasets obtained from Singapore. We also provide an analysis of this ambulatory dataset and demonstrate the accuracy in modeling dependencies of different natures using various machine learning techniques.

Suggested Citation

  • Adrian Xi Lin & Andrew Fu Wah Ho & Kang Hao Cheong & Zengxiang Li & Wentong Cai & Marcel Lucas Chee & Yih Yng Ng & Xiaokui Xiao & Marcus Eng Hock Ong, 2020. "Leveraging Machine Learning Techniques and Engineering of Multi-Nature Features for National Daily Regional Ambulance Demand Prediction," IJERPH, MDPI, vol. 17(11), pages 1-15, June.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:11:p:4179-:d:370390
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    References listed on IDEAS

    as
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    2. Nabil Channouf & Pierre L’Ecuyer & Armann Ingolfsson & Athanassios Avramidis, 2007. "The application of forecasting techniques to modeling emergency medical system calls in Calgary, Alberta," Health Care Management Science, Springer, vol. 10(1), pages 25-45, February.
    3. Finn Lund Henriksen & Per Schorling & Bruno Hansen & Henrik Schakow & Mogens Lytken Larsen, 2016. "FirstAED emergency dispatch, global positioning of community first responders with distinct roles - a solution to reduce the response times and ensuring an AED to early defibrillation in the rural are," International Journal of Networking and Virtual Organisations, Inderscience Enterprises Ltd, vol. 16(1), pages 86-102.
    4. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    5. Fildes, Robert, 1992. "The evaluation of extrapolative forecasting methods," International Journal of Forecasting, Elsevier, vol. 8(1), pages 81-98, June.
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    Cited by:

    1. Marcel Lucas Chee & Marcus Eng Hock Ong & Fahad Javaid Siddiqui & Zhongheng Zhang & Shir Lynn Lim & Andrew Fu Wah Ho & Nan Liu, 2021. "Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review," IJERPH, MDPI, vol. 18(9), pages 1-15, April.
    2. Matteo Ruggeri & Carlo Drago & Chiara Cadeddu & Alessandro Armuzzi & Salvo Leone & Marco Marchetti, 2020. "The Determinants of Out-of-Pocket Expenditure in IBD Italian Patients. Results from the AMICI Survey," IJERPH, MDPI, vol. 17(21), pages 1-14, November.
    3. Lorenzo Gianquintieri & Maria Antonia Brovelli & Andrea Pagliosa & Gabriele Dassi & Piero Maria Brambilla & Rodolfo Bonora & Giuseppe Maria Sechi & Enrico Gianluca Caiani, 2022. "Generating High-Granularity COVID-19 Territorial Early Alerts Using Emergency Medical Services and Machine Learning," IJERPH, MDPI, vol. 19(15), pages 1-19, July.

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