TY - GEN
T1 - Applying Gaussian Mixture Model for Clustering Analysis of Emergency Room Patients Based on Intubation Status
AU - Chen, Po Kuang
AU - Sung, Shih Hsien
AU - Chen, Ling
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - The study, conducted at two regional hospitals in Taichung, Taiwan, aimed to analyze emergency room patient data using Gaussian Mixture Model (GMM) for clustering based on intubation status. Out of 137,722 cases spanning January 1, 2017, to September 30, 2023, 1.14% underwent intubation. The study included the following variables: continuous variables such as WBC (White Blood Cell count), Hb (Hemoglobin), Hct (Hematocrit), MCV (Mean Corpuscular Volume), Blood Sugar, Creatinine levels, HR (Heart Rate), RR (Respiratory Rate), BT (Body Temperature), and SI (shock index). Additionally, categorical variables encompass Gender and Diabetes Mellitus (DM). Patients were divided into Rule In and Rule Out groups, with distinct intubation rates, 2.56% and 0.75%. Rule Out group, with a low intubation rate, identified patients with minimal intubation probability. We can infer that patients with elevated WBC, low Hb, low Hct, high blood sugar, high creatinine, high heart rate, and high shock index are more likely to require intubation compared to patients with normal values. Further research is needed to explore its application.
AB - The study, conducted at two regional hospitals in Taichung, Taiwan, aimed to analyze emergency room patient data using Gaussian Mixture Model (GMM) for clustering based on intubation status. Out of 137,722 cases spanning January 1, 2017, to September 30, 2023, 1.14% underwent intubation. The study included the following variables: continuous variables such as WBC (White Blood Cell count), Hb (Hemoglobin), Hct (Hematocrit), MCV (Mean Corpuscular Volume), Blood Sugar, Creatinine levels, HR (Heart Rate), RR (Respiratory Rate), BT (Body Temperature), and SI (shock index). Additionally, categorical variables encompass Gender and Diabetes Mellitus (DM). Patients were divided into Rule In and Rule Out groups, with distinct intubation rates, 2.56% and 0.75%. Rule Out group, with a low intubation rate, identified patients with minimal intubation probability. We can infer that patients with elevated WBC, low Hb, low Hct, high blood sugar, high creatinine, high heart rate, and high shock index are more likely to require intubation compared to patients with normal values. Further research is needed to explore its application.
KW - Gaussian Mixture Model
KW - clustering analysis
KW - emergency room
KW - intubation
UR - http://www.scopus.com/inward/record.url?scp=85200768813&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-66538-7_1
DO - 10.1007/978-3-031-66538-7_1
M3 - Conference contribution
AN - SCOPUS:85200768813
SN - 9783031665370
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 3
EP - 10
BT - Artificial Intelligence in Medicine - 22nd International Conference, AIME 2024, Proceedings
A2 - Finkelstein, Joseph
A2 - Moskovitch, Robert
A2 - Parimbelli, Enea
PB - Springer Science and Business Media Deutschland GmbH
T2 - 22nd International Conference on Artificial Intelligence in Medicine, AIME 2024
Y2 - 9 July 2024 through 12 July 2024
ER -
