Abstract
Objective: To explore the application of different machine learning models to predict whether patients need endotracheal intubation, and to screen the key indicators by the classifier XGBoost algorithm based on decision tree algorithm. Methods A total of 158 patients with endotracheal intubation and 1003 patients without endotracheal intubation were collected from the first aid database of the General Hospital of the People's Liberation Army of China, and labeled as experimental group and control group respectively. After screening, 53 physiological indicators were obtained, and four classifiers, namely Logistic regression, support vector machine and Adaboost and XGBoost based on weak decision tree classifier, were used to predict the results. Results The average F-score of XGBoost model was 0.8414, and the area under receiver operating characteristic curve (AUROC) was 0.9103, which was the best among the four classifiers. The Adaboost model showed the best performance next, with an F-score of 0.8222 and the AUROC of 0.8935. The two algorithms based on decision tree are superior to Logistic Regression and SVM in this experiment. Conclusion Compared with the prediction model based on Logistic Regression, SVM or Adaboost, the prediction model based on XGBoost algorithm performed better, and could more effectively assist clinicians in the decision of airway management of injured patients.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, B., et al.: Application of CatBoost model based on machine learning in predicting se-vere hand-foot-mouth disease. Chinese J. Infection Control 18(1), 12–16 (2019)
Lin, X., Li, J., Liu, L., Liang, C., Ren, H.L.: Risk prediction models of type 2 diabetic nephropathy. Chin. J. Med. Libr. Inf. Sci. 28(04), 41–45 (2019)
Leng, F., Li, W.: Classification prediction of lung squamous cell carcinoma and lung adenocarcinoma based on XGBoost. J. Capital Med. Univ. 40(06), 889–893 (2019)
Lin, K., Lin, Y., Kong, G.L.: A XGBoost algorithm-based in-hospital mortality prediction model for patients with sepsis in ICU. Chinese J. Health Inf. Manage. 15(05), 536–540 (2018)
Zhao, Y.Z., Cao, D.S., Li, T.S., et al.: Pilot research: construction of emergency rescue database. Chinese Crit. Care Med. 030(006), 609–612 (2018)
Li, T.Q., Zhang, W.J., Xu, S.W.: The value of preventive tracheal intubation in the first-aid of serious severe trauma. Chongqing Med. 29, 4092–4094 (2015)
Hua, H.M.: Experience of popularizing pre-hospital rescue endotracheal intubation technique. Chinese Crit. Care Med. 16(11), 684 (2004)
Chen, H.W., Li, S.S., Zheng, Z.: Early application of endotracheal intubation in patients with severe thoracic trauma. Internal Intensive Med. 12(2), 69–70 (2006)
Pettiford, B.L., Luketich, J.D., Landreneau, R.J.: The management of flail chest. Thorac. Surg. Clin. 17(1), 25–33 (2007)
Hu, J.X., Zhang, G.J.: K-fold cross-validation based selected ensemble classification algorithm. Bull. Sci. Technol. 29(12), 115–117 (2013)
Lei, J.B.: Research on Equipment State Degradation Assessment and Trend Prediction Based on Logical Regression and SVM. Shanghai Jiaotong University, Shanghai (2008)
Chen, K., Zhu, Y.: A summary of machine learning and related algorithms. Stat. Inf. Forum 22(5), 105–112 (2007)
Cao, Y., Miao, Q.G., Liu, J.C., Gao, L.: Advance and prospects of AdaBoost algorithm. Acta Automatica Sinica 39(6), 745–758 (2013)
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.785–794. ACM (2016)
Lian, K.Q.: The Study and Application of Ensemble of Trees Based on Boosting. China University of Geosciences, Beijing (2018)
Wang, R.R.: Research on Machine Learning Methods for Disease Intelligent Diagnosis. East China Jiaotong University, Nanchang (2015)
Acknowledgment
This work was partly supported by the National Key Research and Development Plan for Science and Technology Winter Olympics of the Ministry of Science and Technology of China (2019YFF0302301).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yu, Z., Li, J., Zhao, Y. (2022). Prediction of Airway Management of Trauma Patients Based on Machine Learning. In: Shi, X., Bohács, G., Ma, Y., Gong, D., Shang, X. (eds) LISS 2021. Lecture Notes in Operations Research. Springer, Singapore. https://doi.org/10.1007/978-981-16-8656-6_12
Download citation
DOI: https://doi.org/10.1007/978-981-16-8656-6_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-8655-9
Online ISBN: 978-981-16-8656-6
eBook Packages: Business and ManagementBusiness and Management (R0)