Machine learning‐based patient classification system for adult patients in intensive care units: A cross‐sectional study

Abstract

AIM: This study aimed to develop a patient classification system that stratifies patients admitted to the intensive care unit based on their disease severity and care needs. BACKGROUND: Classifying patients into homogenous groups based on clinical characteristics can optimize nursing care. However, an objective method for determining such groups remains unclear. METHODS: Predictors representing disease severity and nursing workload were considered. Patients were clustered into subgroups with different characteristics based on the results of a clustering algorithm. A patient classification system was developed using a partial least squares regression model. RESULTS: Data of 300 patients were analysed. Cluster analysis identified three subgroups of critically patients with different levels of clinical trajectories. Except for blood potassium levels (p = .29), the subgroups were significantly different according to disease severity and nursing workload. The predicted value ranges of the regression model for Classes A, B and C were <1.44, 1.44-2.03 and >2.03. The model was shown to have good fit and satisfactory prediction efficiency using 200 permutation tests. CONCLUSIONS: Classifying patients based on disease severity and care needs enables the development of tailored nursing management for each subgroup. IMPLICATIONS FOR NURSING MANAGEMENT: The patient classification system can help nurse managers identify homogeneous patient groups and further improve the management of critically ill patients.

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