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

Adrian Xi Lin, Andrew Fu Wah Ho, Kang Hao Cheong, Zengxiang Li, Wentong Cai, Marcel Lucas Chee, Yih Yng Ng, Xiaokui Xiao and Marcus Eng Hock Ong
Additional contact information
Adrian Xi Lin: School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
Andrew Fu Wah Ho: SingHealth Duke-NUS Emergency Medicine Academic Clinical Program, Duke-National University of Singapore Medical School, Singapore 169857, Singapore
Kang Hao Cheong: Science, Mathematics and Technology Cluster, Singapore University of Technology and Design (SUTD), 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
Xiaokui Xiao: School of Computing, National University of Singapore, Singapore 117417, Singapore
Marcus Eng Hock Ong: Health Services & Systems Research, Duke-NUS Medical School, Singapore 169857, Singapore

IJERPH, 2020, vol. 17, issue 11, 1-15

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.

Keywords: demand prediction; ambulance deployment; emergency medical services; health informatics; emergency medicine; geospatial; complexity science; nonlinear dynamics (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1660-4601/17/11/4179/pdf (application/pdf)
https://www.mdpi.com/1660-4601/17/11/4179/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:17:y:2020:i:11:p:4179-:d:370390

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().