Machine Learning for Predictive Modelling of Ambulance Calls

Yu, Miao, Kollias, Dimitrios, Wingate, James , Siriwardena, Niro and Kollias, Stefanos (2021) Machine Learning for Predictive Modelling of Ambulance Calls. Electronics, 10 (4). p. 482. ISSN 2079-9292

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Machine Learning for Predictive Modelling of Ambulance Calls
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Item Type:Article
Item Status:Live Archive


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.

Keywords:predictive modelling, latent information extraction, machine learning, forward model, backward model, ambulance calls, attendance conveyance
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:46528
Deposited On:20 Sep 2021 11:04

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