@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",
}