A new machine learning technique for predicting traumatic injuries outcomes based on the vital signsCitation formats

Standard

A new machine learning technique for predicting traumatic injuries outcomes based on the vital signs. / Almaghrabi, Fatima; Xu, Dong Ling; Yang, Jian Bo.

ICAC 2019 - 2019 25th IEEE International Conference on Automation and Computing. ed. / Hui Yu. IEEE, 2019. 8895012 (ICAC 2019 - 2019 25th IEEE International Conference on Automation and Computing).

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Harvard

Almaghrabi, F, Xu, DL & Yang, JB 2019, A new machine learning technique for predicting traumatic injuries outcomes based on the vital signs. in H Yu (ed.), ICAC 2019 - 2019 25th IEEE International Conference on Automation and Computing., 8895012, ICAC 2019 - 2019 25th IEEE International Conference on Automation and Computing, IEEE, 25th IEEE International Conference on Automation and Computing, ICAC 2019, Lancaster, United Kingdom, 5/09/19. https://doi.org/10.23919/IConAC.2019.8895012

APA

Almaghrabi, F., Xu, D. L., & Yang, J. B. (2019). A new machine learning technique for predicting traumatic injuries outcomes based on the vital signs. In H. Yu (Ed.), ICAC 2019 - 2019 25th IEEE International Conference on Automation and Computing [8895012] (ICAC 2019 - 2019 25th IEEE International Conference on Automation and Computing). IEEE. https://doi.org/10.23919/IConAC.2019.8895012

Vancouver

Almaghrabi F, Xu DL, Yang JB. A new machine learning technique for predicting traumatic injuries outcomes based on the vital signs. In Yu H, editor, ICAC 2019 - 2019 25th IEEE International Conference on Automation and Computing. IEEE. 2019. 8895012. (ICAC 2019 - 2019 25th IEEE International Conference on Automation and Computing). https://doi.org/10.23919/IConAC.2019.8895012

Author

Almaghrabi, Fatima ; Xu, Dong Ling ; Yang, Jian Bo. / A new machine learning technique for predicting traumatic injuries outcomes based on the vital signs. ICAC 2019 - 2019 25th IEEE International Conference on Automation and Computing. editor / Hui Yu. IEEE, 2019. (ICAC 2019 - 2019 25th IEEE International Conference on Automation and Computing).

Bibtex

@inproceedings{d5c4590bb1ba4cf09c217a93f50f012b,
title = "A new machine learning technique for predicting traumatic injuries outcomes based on the vital signs",
abstract = "Traditional vital signs are an essential part of triage assessment in emergency departments (ED), and have been widely used in trauma prediction models. Previous researchers have studied the effect of vital signs scores on predicting traumatic injury outcomes and have found it to be significant. Based on the vital signs' scores, an Interpretable Machine Learning (IML) method is proposed to predict patient outcomes and is compared with various ML algorithms. Results indicate that the IML method has a comparable performance with a mean AUC of 0.683, and its interpretability would help in the early identification of trauma patients at risk of mortality.",
keywords = "Belief rule-based inference, Interpretable machine learning technique, Maximum likelihood evidential reasoning, Trauma outcome prediction, Vital signs",
author = "Fatima Almaghrabi and Xu, {Dong Ling} and Yang, {Jian Bo}",
year = "2019",
month = nov,
day = "11",
doi = "10.23919/IConAC.2019.8895012",
language = "English",
series = "ICAC 2019 - 2019 25th IEEE International Conference on Automation and Computing",
publisher = "IEEE",
editor = "Hui Yu",
booktitle = "ICAC 2019 - 2019 25th IEEE International Conference on Automation and Computing",
address = "United States",
note = "25th IEEE International Conference on Automation and Computing, ICAC 2019 ; Conference date: 05-09-2019 Through 07-09-2019",

}

RIS

TY - GEN

T1 - A new machine learning technique for predicting traumatic injuries outcomes based on the vital signs

AU - Almaghrabi, Fatima

AU - Xu, Dong Ling

AU - Yang, Jian Bo

PY - 2019/11/11

Y1 - 2019/11/11

N2 - Traditional vital signs are an essential part of triage assessment in emergency departments (ED), and have been widely used in trauma prediction models. Previous researchers have studied the effect of vital signs scores on predicting traumatic injury outcomes and have found it to be significant. Based on the vital signs' scores, an Interpretable Machine Learning (IML) method is proposed to predict patient outcomes and is compared with various ML algorithms. Results indicate that the IML method has a comparable performance with a mean AUC of 0.683, and its interpretability would help in the early identification of trauma patients at risk of mortality.

AB - Traditional vital signs are an essential part of triage assessment in emergency departments (ED), and have been widely used in trauma prediction models. Previous researchers have studied the effect of vital signs scores on predicting traumatic injury outcomes and have found it to be significant. Based on the vital signs' scores, an Interpretable Machine Learning (IML) method is proposed to predict patient outcomes and is compared with various ML algorithms. Results indicate that the IML method has a comparable performance with a mean AUC of 0.683, and its interpretability would help in the early identification of trauma patients at risk of mortality.

KW - Belief rule-based inference

KW - Interpretable machine learning technique

KW - Maximum likelihood evidential reasoning

KW - Trauma outcome prediction

KW - Vital signs

UR - http://www.scopus.com/inward/record.url?scp=85075775171&partnerID=8YFLogxK

U2 - 10.23919/IConAC.2019.8895012

DO - 10.23919/IConAC.2019.8895012

M3 - Conference contribution

T3 - ICAC 2019 - 2019 25th IEEE International Conference on Automation and Computing

BT - ICAC 2019 - 2019 25th IEEE International Conference on Automation and Computing

A2 - Yu, Hui

PB - IEEE

T2 - 25th IEEE International Conference on Automation and Computing, ICAC 2019

Y2 - 5 September 2019 through 7 September 2019

ER -