Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Machine Learning for Predictive Modelling of Ambulance Calls

Version 1 : Received: 1 January 2021 / Approved: 4 January 2021 / Online: 4 January 2021 (16:44:11 CET)

A peer-reviewed article of this Preprint also exists.

Yu, M.; Kollias, D.; Wingate, J.; Siriwardena, N.; Kollias, S. Machine Learning for Predictive Modelling of Ambulance Calls. Electronics 2021, 10, 482. Yu, M.; Kollias, D.; Wingate, J.; Siriwardena, N.; Kollias, S. Machine Learning for Predictive Modelling of Ambulance Calls. Electronics 2021, 10, 482.

Journal reference: Electronics 2021, 10, 482
DOI: 10.3390/electronics10040482

Abstract

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.

Subject Areas

predictive modelling; latent information extraction; machine learning; forward model; backward model; ambulance calls; attendance; conveyance

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