Machine learning for predictive modelling of ambulance calls

Yu, Miao, Kollias, Dimitrios, Wingate, James , Siriwardena, Aloysius Niroshan 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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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, conveyance, attendance, latent information extraction, backward model, forward model, ambulance calls, machine learning
Subjects:B Subjects allied to Medicine > B990 Subjects Allied to Medicine not elsewhere classified
B Subjects allied to Medicine > B780 Paramedical Nursing
A Medicine and Dentistry > A300 Clinical Medicine
Divisions:College of Social Science > School of Health & Social Care
ID Code:44651
Deposited On:27 Apr 2021 09:55

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