ArticleInternational journal of environmental research and public health2020
Leveraging Machine Learning Techniques and Engineering of Multi-Nature Features for National Daily Regional Ambulance Demand Prediction.
Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 citations in OpenAlex.
- Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review.International journal of environmental research and public health · 2021Pooled it
- Use of artificial intelligence in the out-of-hospital care settings: a scoping review.BMJ digital health & AI · 2026Article
- RETRACTED: Artificial intelligence for emergency medical care.Health care science · 2025Review
- Artificial intelligence and machine learning in prehospital emergency care: A scoping review.iScience · 2023Article
- The Aspects of Running Artificial Intelligence in Emergency Care; a Scoping Review.Archives of academic emergency medicine · 2023Article
- Demand prediction of medical services in home and community-based services for older adults in China using machine learning.Frontiers in public health · 2023Article
- Generating High-Granularity COVID-19 Territorial Early Alerts Using Emergency Medical Services and Machine Learning.International journal of environmental research and public health · 2022Article
- Prediction Model of Deep Learning for Ambulance Transports in Kesennuma City by Meteorological Data.Open access emergency medicine : OAEM · 2021Article
- Knowing in Nurses' Belief and Attitude about Patient Activation: A Validation of the Korean Clinician Support for Patient Activation Measure Using Rasch Analysis.Healthcare (Basel, Switzerland) · 2020Article
- The Determinants of Out-of-Pocket Expenditure in IBD Italian Patients. Results from the AMICI Survey.International journal of environmental research and public health · 2020Article
Corrections and comments
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Authors and funding
9 authors at 8 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.