Machine learning based electronic triage for emergency department

Diana Olivia; Ashalatha Nayak; Mamatha Balachandra

doi:10.1007/978-981-13-2907-4_19

Machine learning based electronic triage for emergency department

Diana Olivia, Ashalatha Nayak, Mamatha Balachandra

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

Increase in the number of casualties visit to Emergency Department (ED) have lead to over crowd and delay in medical care. Hence, electronic triaging has been deployed to alleviate these problems and improve managing the patient. In this paper research methodology framework based on diagnostic and cross-sectional study is used for patient triage. The empirical approach is used to build models for patient triage to correctly predict the patient’s medical condition, given their signs and symptoms. Models are built with supervised learning algorithms. The “Naive Bayes”, “Support Vector Machine”, “Decision Tree” and, “Neural Network” classification models are implemented and evaluated using chi-square statistical test. This study infers the significance of using machine learning algorithms to predict patient’s medical condition. Support Vector Machine and Decision Tree have shown better performance for the considered dataset.

Original language	English
Title of host publication	Applications and Techniques in Information Security - 9th International Conference, ATIS 2018, Proceedings
Editors	Qingfeng Chen, Jia Wu, Shichao Zhang, Changan Yuan, Lynn Batten, Gang Li
Publisher	Springer Verlag
Pages	215-221
Number of pages	7
ISBN (Print)	9789811329067
DOIs	https://doi.org/10.1007/978-981-13-2907-4_19
Publication status	Published - 01-01-2018
Event	9th International Conference on Applications and Techniques in Information Security, ATIS 2018 - Nanning, China Duration: 09-11-2018 → 11-11-2018

Publication series

Name	Communications in Computer and Information Science
Volume	950
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	9th International Conference on Applications and Techniques in Information Security, ATIS 2018
Country/Territory	China
City	Nanning
Period	09-11-18 → 11-11-18

All Science Journal Classification (ASJC) codes

Computer Science(all)
Mathematics(all)

Access to Document

10.1007/978-981-13-2907-4_19

Cite this

Olivia, D., Nayak, A., & Balachandra, M. (2018). Machine learning based electronic triage for emergency department. In Q. Chen, J. Wu, S. Zhang, C. Yuan, L. Batten, & G. Li (Eds.), Applications and Techniques in Information Security - 9th International Conference, ATIS 2018, Proceedings (pp. 215-221). (Communications in Computer and Information Science; Vol. 950). Springer Verlag. https://doi.org/10.1007/978-981-13-2907-4_19

Olivia, Diana ; Nayak, Ashalatha ; Balachandra, Mamatha. / Machine learning based electronic triage for emergency department. Applications and Techniques in Information Security - 9th International Conference, ATIS 2018, Proceedings. editor / Qingfeng Chen ; Jia Wu ; Shichao Zhang ; Changan Yuan ; Lynn Batten ; Gang Li. Springer Verlag, 2018. pp. 215-221 (Communications in Computer and Information Science).

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title = "Machine learning based electronic triage for emergency department",

abstract = "Increase in the number of casualties visit to Emergency Department (ED) have lead to over crowd and delay in medical care. Hence, electronic triaging has been deployed to alleviate these problems and improve managing the patient. In this paper research methodology framework based on diagnostic and cross-sectional study is used for patient triage. The empirical approach is used to build models for patient triage to correctly predict the patient{\textquoteright}s medical condition, given their signs and symptoms. Models are built with supervised learning algorithms. The “Naive Bayes”, “Support Vector Machine”, “Decision Tree” and, “Neural Network” classification models are implemented and evaluated using chi-square statistical test. This study infers the significance of using machine learning algorithms to predict patient{\textquoteright}s medical condition. Support Vector Machine and Decision Tree have shown better performance for the considered dataset.",

author = "Diana Olivia and Ashalatha Nayak and Mamatha Balachandra",

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booktitle = "Applications and Techniques in Information Security - 9th International Conference, ATIS 2018, Proceedings",

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Olivia, D , Nayak, A & Balachandra, M 2018, Machine learning based electronic triage for emergency department. in Q Chen, J Wu, S Zhang, C Yuan, L Batten & G Li (eds), Applications and Techniques in Information Security - 9th International Conference, ATIS 2018, Proceedings. Communications in Computer and Information Science, vol. 950, Springer Verlag, pp. 215-221, 9th International Conference on Applications and Techniques in Information Security, ATIS 2018, Nanning, China, 09-11-18. https://doi.org/10.1007/978-981-13-2907-4_19

Machine learning based electronic triage for emergency department. / Olivia, Diana ; Nayak, Ashalatha ; Balachandra, Mamatha.

Applications and Techniques in Information Security - 9th International Conference, ATIS 2018, Proceedings. ed. / Qingfeng Chen; Jia Wu; Shichao Zhang; Changan Yuan; Lynn Batten; Gang Li. Springer Verlag, 2018. p. 215-221 (Communications in Computer and Information Science; Vol. 950).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Nayak, Ashalatha

AU - Balachandra, Mamatha

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N2 - Increase in the number of casualties visit to Emergency Department (ED) have lead to over crowd and delay in medical care. Hence, electronic triaging has been deployed to alleviate these problems and improve managing the patient. In this paper research methodology framework based on diagnostic and cross-sectional study is used for patient triage. The empirical approach is used to build models for patient triage to correctly predict the patient’s medical condition, given their signs and symptoms. Models are built with supervised learning algorithms. The “Naive Bayes”, “Support Vector Machine”, “Decision Tree” and, “Neural Network” classification models are implemented and evaluated using chi-square statistical test. This study infers the significance of using machine learning algorithms to predict patient’s medical condition. Support Vector Machine and Decision Tree have shown better performance for the considered dataset.

AB - Increase in the number of casualties visit to Emergency Department (ED) have lead to over crowd and delay in medical care. Hence, electronic triaging has been deployed to alleviate these problems and improve managing the patient. In this paper research methodology framework based on diagnostic and cross-sectional study is used for patient triage. The empirical approach is used to build models for patient triage to correctly predict the patient’s medical condition, given their signs and symptoms. Models are built with supervised learning algorithms. The “Naive Bayes”, “Support Vector Machine”, “Decision Tree” and, “Neural Network” classification models are implemented and evaluated using chi-square statistical test. This study infers the significance of using machine learning algorithms to predict patient’s medical condition. Support Vector Machine and Decision Tree have shown better performance for the considered dataset.

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ER -

Olivia D , Nayak A , Balachandra M. Machine learning based electronic triage for emergency department. In Chen Q, Wu J, Zhang S, Yuan C, Batten L, Li G, editors, Applications and Techniques in Information Security - 9th International Conference, ATIS 2018, Proceedings. Springer Verlag. 2018. p. 215-221. (Communications in Computer and Information Science). doi: 10.1007/978-981-13-2907-4_19

Machine learning based electronic triage for emergency department

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