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

Journal of Nursing ManagementVolume 29, Issue 6 p. 1752-1762 ORIGINAL ARTICLE Machine learning-based patient classification system for adult patients in intensive care units: A cross-sectional study Ran An RN, Master Student Nursing School, Harbin Medical University, Harbin, ChinaSearch for more papers by this authorGuang-ming Chang MN, RN, The Party Committee, The Second Affiliated Hospital of Harbin Medical University, Harbin, ChinaSearch for more papers by this authorYu-ying Fan PhD, Corresponding Author Professor fanfanfensituan@126.com orcid.org/0000-0003-3403-2580 Nursing School, Harbin Medical University, Harbin, China Correspondence Yu-ying Fan, Harbin Medical University, No.194, Bao Jian Road, Nan-Gang District, Harbin, 150086,China. Email: fanfanfensituan@126.comSearch for more papers by this authorLing-ling Ji BN,RN, Department of Pediatrics, The Second Affiliated Hospital of Harbin Medical University, Harbin, ChinaSearch for more papers by this authorXiao-hui Wang MN,RN, Department of Intensive Care Unit, The Second Affiliated Hospital of Harbin Medical University, Harbin, ChinaSearch for more papers by this authorSu Hong PHD,RN, Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaSearch for more papers by this author Ran An RN, Master Student Nursing School, Harbin Medical University, Harbin, ChinaSearch for more papers by this authorGuang-ming Chang MN, RN, The Party Committee, The Second Affiliated Hospital of Harbin Medical University, Harbin, ChinaSearch for more papers by this authorYu-ying Fan PhD, Corresponding Author Professor fanfanfensituan@126.com orcid.org/0000-0003-3403-2580 Nursing School, Harbin Medical University, Harbin, China Correspondence Yu-ying Fan, Harbin Medical University, No.194, Bao Jian Road, Nan-Gang District, Harbin, 150086,China. Email: fanfanfensituan@126.comSearch for more papers by this authorLing-ling Ji BN,RN, Department of Pediatrics, The Second Affiliated Hospital of Harbin Medical University, Harbin, ChinaSearch for more papers by this authorXiao-hui Wang MN,RN, Department of Intensive Care Unit, The Second Affiliated Hospital of Harbin Medical University, Harbin, ChinaSearch for more papers by this authorSu Hong PHD,RN, Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaSearch for more papers by this author First published: 10 February 2021 https://doi.org/10.1111/jonm.13284 Guang-ming Chang and Ling-ling Ji are co-authors. Funding information: This study was supported by the National Natural Science Foundation of China, grant number 71704040. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat 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. Open Research DATA AVAILABILITY STATEMENT The datasets consulted during this study are available from the corresponding author on reasonable request. Supporting Information Filename Description jonm13284-sup-0001-TableS1-S2.docxWord document, 20.3 KB Table S1-S2 Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article. Volume29, Issue6September 2021Pages 1752-1762 RelatedInformation