Unsupervised Learning Approaches for Identifying ICU Patient Subgroups: Do Results Generalise?
This is an incremental study that addresses the problem of ICU efficiency for healthcare providers by showing that standardised restructuring may not be feasible due to ICU variation.
The study tested whether common patient subgroups exist across different ICUs using unsupervised learning to identify subgroups based on medical resource need, but found limited similarities between datasets, providing evidence against the hypothesis.
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.