Using machine learning to improve surgical outcomes

Sindhura Bonthu; Priscila Rodrigues Armijo; Tiffany Tanner; Qiuming Zhu

doi:10.1109/ICMLA.2019.00233

Using machine learning to improve surgical outcomes

Sindhura Bonthu, Priscila Rodrigues Armijo, Tiffany Tanner, Qiuming Zhu

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

3 Scopus citations

Abstract

Predicting the severity of patient's condition helps providing accurate clinical care. Mortality prediction is one of the challenges due to distinct characteristics of the patient's data. It is a challenging problem to evaluate the patient's data which is highly sparse, highly biased and imbalanced, and highly mixed. In this paper, we are focusing on processing large volumes of data using neural networks which can be further used for analysis to obtain useful insights, such as identifying the major features contributing to certain outcomes of events or classifying different objects based on the presences of certain attributes and their measurements.

Original language	English (US)
Title of host publication	Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019
Editors	M. Arif Wani, Taghi M. Khoshgoftaar, Dingding Wang, Huanjing Wang, Naeem Seliya
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1426-1431
Number of pages	6
ISBN (Electronic)	9781728145495
DOIs	https://doi.org/10.1109/ICMLA.2019.00233
State	Published - Dec 2019
Event	18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019 - Boca Raton, United States Duration: Dec 16 2019 → Dec 19 2019

Publication series

Name	Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019

Conference

Conference	18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019
Country/Territory	United States
City	Boca Raton
Period	12/16/19 → 12/19/19

Keywords

Machine-Learning
Medical-data-analysis
Neural-Networks
Resampling
Sparse-and-Imbalanced

ASJC Scopus subject areas

Strategy and Management
Artificial Intelligence
Computer Science Applications
Decision Sciences (miscellaneous)
Signal Processing
Media Technology

Access to Document

10.1109/ICMLA.2019.00233

Cite this

Bonthu, S., Rodrigues Armijo, P., Tanner, T., & Zhu, Q. (2019). Using machine learning to improve surgical outcomes. In M. A. Wani, T. M. Khoshgoftaar, D. Wang, H. Wang, & N. Seliya (Eds.), Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019 (pp. 1426-1431). Article 8999132 (Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLA.2019.00233

Using machine learning to improve surgical outcomes. / Bonthu, Sindhura; Rodrigues Armijo, Priscila ; Tanner, Tiffany et al.
Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019. ed. / M. Arif Wani; Taghi M. Khoshgoftaar; Dingding Wang; Huanjing Wang; Naeem Seliya. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1426-1431 8999132 (Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019).

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

Bonthu, S, Rodrigues Armijo, P , Tanner, T & Zhu, Q 2019, Using machine learning to improve surgical outcomes. in MA Wani, TM Khoshgoftaar, D Wang, H Wang & N Seliya (eds), Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019., 8999132, Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019, Institute of Electrical and Electronics Engineers Inc., pp. 1426-1431, 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019, Boca Raton, United States, 12/16/19. https://doi.org/10.1109/ICMLA.2019.00233

Bonthu S, Rodrigues Armijo P , Tanner T, Zhu Q. Using machine learning to improve surgical outcomes. In Wani MA, Khoshgoftaar TM, Wang D, Wang H, Seliya N, editors, Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1426-1431. 8999132. (Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019). doi: 10.1109/ICMLA.2019.00233

Bonthu, Sindhura ; Rodrigues Armijo, Priscila ; Tanner, Tiffany et al. / Using machine learning to improve surgical outcomes. Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019. editor / M. Arif Wani ; Taghi M. Khoshgoftaar ; Dingding Wang ; Huanjing Wang ; Naeem Seliya. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1426-1431 (Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019).

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abstract = "Predicting the severity of patient's condition helps providing accurate clinical care. Mortality prediction is one of the challenges due to distinct characteristics of the patient's data. It is a challenging problem to evaluate the patient's data which is highly sparse, highly biased and imbalanced, and highly mixed. In this paper, we are focusing on processing large volumes of data using neural networks which can be further used for analysis to obtain useful insights, such as identifying the major features contributing to certain outcomes of events or classifying different objects based on the presences of certain attributes and their measurements.",

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author = "Sindhura Bonthu and {Rodrigues Armijo}, Priscila and Tiffany Tanner and Qiuming Zhu",

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Using machine learning to improve surgical outcomes

Abstract

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Keywords

ASJC Scopus subject areas

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