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.