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Article

Machine Learning for Predictive Modelling of Ambulance Calls

1
School of Computer Science, University of Lincoln, Lincoln LN67TS, UK
2
School of Computing and Mathematical Sciences, University of Greenwich, London SE10 9LS, UK
3
School of Health and Social Care, University of Lincoln, Lincoln LN67TS, UK
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Jian Sun
Electronics 2021, 10(4), 482; https://doi.org/10.3390/electronics10040482
Received: 1 January 2021 / Revised: 31 January 2021 / Accepted: 9 February 2021 / Published: 18 February 2021
A novel machine learning approach is presented in this paper, based on extracting latent information and using it to assist decision making on ambulance attendance and conveyance to a hospital. The approach includes two steps: in the first, a forward model analyzes the clinical and, possibly, non-clinical factors (explanatory variables), predicting whether positive decisions (response variables) should be given to the ambulance call, or not; in the second, a backward model analyzes the latent variables extracted from the forward model to infer the decision making procedure. The forward model is implemented through a machine, or deep learning technique, whilst the backward model is implemented through unsupervised learning. An experimental study is presented, which illustrates the obtained results, by investigating emergency ambulance calls to people in nursing and residential care homes, over a one-year period, using an anonymized data set provided by East Midlands Ambulance Service in United Kingdom. View Full-Text
Keywords: predictive modelling; latent information extraction; machine learning; forward model; backward model; ambulance calls; attendance; conveyance predictive modelling; latent information extraction; machine learning; forward model; backward model; ambulance calls; attendance; conveyance
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MDPI and ACS Style

Yu, M.; Kollias, D.; Wingate, J.; Siriwardena, N.; Kollias, S. Machine Learning for Predictive Modelling of Ambulance Calls. Electronics 2021, 10, 482. https://doi.org/10.3390/electronics10040482

AMA Style

Yu M, Kollias D, Wingate J, Siriwardena N, Kollias S. Machine Learning for Predictive Modelling of Ambulance Calls. Electronics. 2021; 10(4):482. https://doi.org/10.3390/electronics10040482

Chicago/Turabian Style

Yu, Miao, Dimitrios Kollias, James Wingate, Niro Siriwardena, and Stefanos Kollias. 2021. "Machine Learning for Predictive Modelling of Ambulance Calls" Electronics 10, no. 4: 482. https://doi.org/10.3390/electronics10040482

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