Improving Septic Shock Prediction with AdaBoost and Cox Regression Model

Aiman Darwiche; Ayman El-Geneidy; Sumitra Mukherjee

doi:10.1109/ICCECE51280.2021.9342457

Improving Septic Shock Prediction with AdaBoost and Cox Regression Model

Aiman Darwiche
, Ayman El-Geneidy
, Sumitra Mukherjee

Nova Southeastern University

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

Abstract

Septic shock in the advanced state of sepsis, which is a dangerous organ dysfunction disease that happens when the body responds in a dysregulated way to infectious diseases. Sepsis is hard to discover early on, and is difficult to treat if not detected sooner, hence, leading to high mortality rates. The efforts to improve the methods for identifying septic shock is ongoing in the medical and computer science communities. This paper uses the MMIC-III database to create a model to effectively predict septic shock utilizing a combination of the Cox regression model and AdaBoost. The prediction model is constructed by acquiring a risk factor score using Cox regression on various septic shock indicators. The score was appended as a feature to a selected listing of indicators and the AdaBoost ensemble classifier was applied to deliver the model. The predictive accuracy of the Cox Enhanced AdaBoost (CEAB) model was compared to prominent models to evaluate its effectiveness.

Original language	English
Title of host publication	2021 IEEE International Conference on Consumer Electronics and Computer Engineering, ICCECE 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	522-527
Number of pages	6
ISBN (Electronic)	9781728183190
ISBN (Print)	9781728183190
DOIs	https://doi.org/10.1109/ICCECE51280.2021.9342457
State	Published - Jan 15 2021
Event	2021 IEEE International Conference on Consumer Electronics and Computer Engineering, ICCECE 2021 - Virtual, Guangzhou, China Duration: Jan 15 2021 → Jan 17 2021

Publication series

Name	2021 IEEE International Conference on Consumer Electronics and Computer Engineering, ICCECE 2021

Conference

Conference	2021 IEEE International Conference on Consumer Electronics and Computer Engineering, ICCECE 2021
Country/Territory	China
City	Virtual, Guangzhou
Period	1/15/21 → 1/17/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

ASJC Scopus Subject Areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Hardware and Architecture
Instrumentation
Electrical and Electronic Engineering

Keywords

AdaBoost
Artificial Intelligence
Classification
Cox Regression Model
Ensemble Classifier
Machine Learning
Prediction
Sepsis
Septic Shock

Access to Document

10.1109/ICCECE51280.2021.9342457

Cite this

Darwiche, A., El-Geneidy, A., & Mukherjee, S. (2021). Improving Septic Shock Prediction with AdaBoost and Cox Regression Model. In 2021 IEEE International Conference on Consumer Electronics and Computer Engineering, ICCECE 2021 (pp. 522-527). Article 9342457 (2021 IEEE International Conference on Consumer Electronics and Computer Engineering, ICCECE 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCECE51280.2021.9342457

Improving Septic Shock Prediction with AdaBoost and Cox Regression Model. / Darwiche, Aiman; El-Geneidy, Ayman; Mukherjee, Sumitra.
2021 IEEE International Conference on Consumer Electronics and Computer Engineering, ICCECE 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 522-527 9342457 (2021 IEEE International Conference on Consumer Electronics and Computer Engineering, ICCECE 2021).

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

Darwiche, A, El-Geneidy, A & Mukherjee, S 2021, Improving Septic Shock Prediction with AdaBoost and Cox Regression Model. in 2021 IEEE International Conference on Consumer Electronics and Computer Engineering, ICCECE 2021., 9342457, 2021 IEEE International Conference on Consumer Electronics and Computer Engineering, ICCECE 2021, Institute of Electrical and Electronics Engineers Inc., pp. 522-527, 2021 IEEE International Conference on Consumer Electronics and Computer Engineering, ICCECE 2021, Virtual, Guangzhou, China, 1/15/21. https://doi.org/10.1109/ICCECE51280.2021.9342457

Darwiche A, El-Geneidy A, Mukherjee S. Improving Septic Shock Prediction with AdaBoost and Cox Regression Model. In 2021 IEEE International Conference on Consumer Electronics and Computer Engineering, ICCECE 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 522-527. 9342457. (2021 IEEE International Conference on Consumer Electronics and Computer Engineering, ICCECE 2021). doi: 10.1109/ICCECE51280.2021.9342457

Darwiche, Aiman ; El-Geneidy, Ayman ; Mukherjee, Sumitra. / Improving Septic Shock Prediction with AdaBoost and Cox Regression Model. 2021 IEEE International Conference on Consumer Electronics and Computer Engineering, ICCECE 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 522-527 (2021 IEEE International Conference on Consumer Electronics and Computer Engineering, ICCECE 2021).

@inproceedings{3f305733d91746669e5be0611137609b,

title = "Improving Septic Shock Prediction with AdaBoost and Cox Regression Model",

abstract = "Septic shock in the advanced state of sepsis, which is a dangerous organ dysfunction disease that happens when the body responds in a dysregulated way to infectious diseases. Sepsis is hard to discover early on, and is difficult to treat if not detected sooner, hence, leading to high mortality rates. The efforts to improve the methods for identifying septic shock is ongoing in the medical and computer science communities. This paper uses the MMIC-III database to create a model to effectively predict septic shock utilizing a combination of the Cox regression model and AdaBoost. The prediction model is constructed by acquiring a risk factor score using Cox regression on various septic shock indicators. The score was appended as a feature to a selected listing of indicators and the AdaBoost ensemble classifier was applied to deliver the model. The predictive accuracy of the Cox Enhanced AdaBoost (CEAB) model was compared to prominent models to evaluate its effectiveness. ",

keywords = "AdaBoost, Artificial Intelligence, Classification, Cox Regression Model, Ensemble Classifier, Machine Learning, Prediction, Sepsis, Septic Shock",

author = "Aiman Darwiche and Ayman El-Geneidy and Sumitra Mukherjee",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Consumer Electronics and Computer Engineering, ICCECE 2021 ; Conference date: 15-01-2021 Through 17-01-2021",

year = "2021",

month = jan,

day = "15",

doi = "10.1109/ICCECE51280.2021.9342457",

language = "English",

isbn = "9781728183190",

series = "2021 IEEE International Conference on Consumer Electronics and Computer Engineering, ICCECE 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "522--527",

booktitle = "2021 IEEE International Conference on Consumer Electronics and Computer Engineering, ICCECE 2021",

}

TY - GEN

T1 - Improving Septic Shock Prediction with AdaBoost and Cox Regression Model

AU - Darwiche, Aiman

AU - El-Geneidy, Ayman

AU - Mukherjee, Sumitra

PY - 2021/1/15

Y1 - 2021/1/15

N2 - Septic shock in the advanced state of sepsis, which is a dangerous organ dysfunction disease that happens when the body responds in a dysregulated way to infectious diseases. Sepsis is hard to discover early on, and is difficult to treat if not detected sooner, hence, leading to high mortality rates. The efforts to improve the methods for identifying septic shock is ongoing in the medical and computer science communities. This paper uses the MMIC-III database to create a model to effectively predict septic shock utilizing a combination of the Cox regression model and AdaBoost. The prediction model is constructed by acquiring a risk factor score using Cox regression on various septic shock indicators. The score was appended as a feature to a selected listing of indicators and the AdaBoost ensemble classifier was applied to deliver the model. The predictive accuracy of the Cox Enhanced AdaBoost (CEAB) model was compared to prominent models to evaluate its effectiveness.

AB - Septic shock in the advanced state of sepsis, which is a dangerous organ dysfunction disease that happens when the body responds in a dysregulated way to infectious diseases. Sepsis is hard to discover early on, and is difficult to treat if not detected sooner, hence, leading to high mortality rates. The efforts to improve the methods for identifying septic shock is ongoing in the medical and computer science communities. This paper uses the MMIC-III database to create a model to effectively predict septic shock utilizing a combination of the Cox regression model and AdaBoost. The prediction model is constructed by acquiring a risk factor score using Cox regression on various septic shock indicators. The score was appended as a feature to a selected listing of indicators and the AdaBoost ensemble classifier was applied to deliver the model. The predictive accuracy of the Cox Enhanced AdaBoost (CEAB) model was compared to prominent models to evaluate its effectiveness.

KW - AdaBoost

KW - Artificial Intelligence

KW - Classification

KW - Cox Regression Model

KW - Ensemble Classifier

KW - Machine Learning

KW - Prediction

KW - Sepsis

KW - Septic Shock

U2 - 10.1109/ICCECE51280.2021.9342457

DO - 10.1109/ICCECE51280.2021.9342457

M3 - Conference contribution

AN - SCOPUS:85101220666

SN - 9781728183190

T3 - 2021 IEEE International Conference on Consumer Electronics and Computer Engineering, ICCECE 2021

SP - 522

EP - 527

BT - 2021 IEEE International Conference on Consumer Electronics and Computer Engineering, ICCECE 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE International Conference on Consumer Electronics and Computer Engineering, ICCECE 2021

Y2 - 15 January 2021 through 17 January 2021

ER -

Improving Septic Shock Prediction with AdaBoost and Cox Regression Model

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus Subject Areas

Keywords

Access to Document

Fingerprint

Cite this