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

Fatima Almaghrabi, Dong Ling Xu, Jian Bo Yang

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

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.

Original languageEnglish
Title of host publicationICAC 2019 - 2019 25th IEEE International Conference on Automation and Computing
EditorsHui Yu
PublisherIEEE
ISBN (Electronic)9781861376664
DOIs
Publication statusE-pub ahead of print - 11 Nov 2019
Event25th IEEE International Conference on Automation and Computing, ICAC 2019 - Lancaster, United Kingdom
Duration: 5 Sept 20197 Sept 2019

Publication series

NameICAC 2019 - 2019 25th IEEE International Conference on Automation and Computing

Conference

Conference25th IEEE International Conference on Automation and Computing, ICAC 2019
Country/TerritoryUnited Kingdom
CityLancaster
Period5/09/197/09/19

Keywords

  • Belief rule-based inference
  • Interpretable machine learning technique
  • Maximum likelihood evidential reasoning
  • Trauma outcome prediction
  • Vital signs

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