Unsupervised Learning Approaches for Identifying ICU Patient Subgroups:
  Do Results Generalise?
Harry Mayne Guy Parsons Adam Mahdi
The use of unsupervised learning to identify patient subgroups has emerged as
a potentially promising direction to improve the efficiency of Intensive Care
Units (ICUs). By identifying subgroups of patients with similar levels of
medical resource need, ICUs could be restructured into a collection of smaller
subunits, each catering to a specific group. However, it is unclear whether
common patient subgroups exist across different ICUs, which would determine
whether ICU restructuring could be operationalised in a standardised manner. In
this paper, we tested the hypothesis that common ICU patient subgroups exist by
examining whether the results from one existing study generalise to a different
dataset. We extracted 16 features representing medical resource need and used
consensus clustering to derive patient subgroups, replicating the previous
study. We found limited similarities between our results and those of the
previous study, providing evidence against the hypothesis. Our findings imply
that there is significant variation between ICUs; thus, a standardised
restructuring approach is unlikely to be appropriate. Instead, potential
efficiency gains might be greater when the number and nature of the subunits
are tailored to each ICU individually.