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.

翻译：利用无监督学习识别患者亚群，已成为提升重症监护室（ICU）效率的潜在方向。通过识别具有相似医疗资源需求水平的患者亚群，可将ICU重组为多个小型单元，每个单元负责特定患者群体。然而，不同ICU之间是否存在共同的患者亚群尚不明确，这决定了ICU重组能否以标准化方式实施。本文通过检验现有研究结果能否推广至不同数据集，验证了“存在共性ICU患者亚群”这一假设。我们提取了代表医疗资源需求的16项特征，并采用共识聚类法推导患者亚群，以复现先前研究。结果显示，我们的结果与先前研究之间相似性有限，这为反对上述假设提供了依据。研究结果表明，不同ICU之间存在显著差异，因此标准化重组方案可能并不适用。相反，若根据各ICU具体情况定制子单元的数量与性质，或将获得更大的潜在效率提升。