Sookyung Hyun1, Pacharmon Kaewprag2, Cheryl Cooper3, Brenda Hixon4, Susan Moffatt-Bruce5. 1. College of Nursing, Pusan National University, 49 Busandaehak-ro Mulgeum-eup, Yangsan-si, 50612, South Korea. Electronic address: sookyung.hyun@pusan.ac.kr. 2. Department of Computer Engineering, Ramkhamhaeng University, Bangkok, Thailand. 3. Central Quality and Education, The Ohio State University Wexner Medical Center, Ohio, United States. 4. Department of Health Services Nursing Education, The Ohio State University Wexner Medical Center, Ohio, United States. 5. Department of Surgery, The Ohio State University Wexner Medical Center, Columbus, Ohio, United States.
Abstract
BACKGROUND AND OBJECTIVE: Identification of subgroups may be useful to understand the clinical characteristics of ICU patients. The purposes of this study were to apply an unsupervised machine learning method to ICU patient data to discover subgroups among them; and to examine their clinical characteristics, therapeutic procedures conducted during the ICU stay, and discharge dispositions. METHODS: K-means clustering method was used with 1503 observations and 9 types of laboratory test results as features. RESULTS: Three clusters were identified from this specific population. Blood urea nitrogen, creatinine, potassium, hemoglobin, and red blood cell were distinctive between the clusters. Cluster Three presented the highest blood products transfusion rate (19.8%), followed by Cluster One (15.5%) and cluster Two (9.3%), which was significantly different. Hemodialysis was more frequently provided to Cluster Three while bronchoscopy was done to Cluster One and Two. Cluster Three showed the highest mortality (30.4%), which was more than two-fold compared to Cluster One (14.1%) and Two (12.2%). CONCLUSION: Three subgroups were identified and their clinical characteristics were compared. These findings may be useful to anticipate treatment strategies and probable outcomes of ICU patients. Unsupervised machine learning may enable ICU multi-dimensional data to be organized and to make sense of the data.
BACKGROUND AND OBJECTIVE: Identification of subgroups may be useful to understand the clinical characteristics of ICU patients. The purposes of this study were to apply an unsupervised machine learning method to ICU patient data to discover subgroups among them; and to examine their clinical characteristics, therapeutic procedures conducted during the ICU stay, and discharge dispositions. METHODS: K-means clustering method was used with 1503 observations and 9 types of laboratory test results as features. RESULTS: Three clusters were identified from this specific population. Blood ureanitrogen, creatinine, potassium, hemoglobin, and red blood cell were distinctive between the clusters. Cluster Three presented the highest blood products transfusion rate (19.8%), followed by Cluster One (15.5%) and cluster Two (9.3%), which was significantly different. Hemodialysis was more frequently provided to Cluster Three while bronchoscopy was done to Cluster One and Two. Cluster Three showed the highest mortality (30.4%), which was more than two-fold compared to Cluster One (14.1%) and Two (12.2%). CONCLUSION: Three subgroups were identified and their clinical characteristics were compared. These findings may be useful to anticipate treatment strategies and probable outcomes of ICU patients. Unsupervised machine learning may enable ICU multi-dimensional data to be organized and to make sense of the data.