Premium
Machine learning‐based patient classification system for adult patients in intensive care units: A cross‐sectional study
Author(s) -
An Ran,
Chang Guangming,
Fan Yuying,
Ji Lingling,
Wang Xiaohui,
Hong Su
Publication year - 2021
Publication title -
journal of nursing management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.925
H-Index - 76
eISSN - 1365-2834
pISSN - 0966-0429
DOI - 10.1111/jonm.13284
Subject(s) - workload , medicine , intensive care unit , nursing management , disease , nursing care , intensive care medicine , nursing , computer science , operating system
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.