A machine learning based approach for identifying traumatic brain injury patients for whom a head CT scan can be avoided

Somayeh Molaei, Frederick K. Korley, S. M.Reza Soroushmehr, Hayley Falk, Haris Sair, Kevin Ward, Kayvan Najarian

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Head CT scan is more often used to evaluate patients with suspected traumatic brain injury (TBI). However, the use of head CT scans in evaluating TBI is costly with low value endeavor. In this paper, we propose a new algorithm and a set of features to help clinicians determine which patients evaluated for TBI need a head CT scan using cost sensitive random forest (CSRF) classifier. We show that random forest (RF) and CSRF are useful methods for identifying patients likely to have a positive head CT scan. The proposed algorithm has superior diagnostic accuracy in comparison to the Canadian head CT algorithm, which is currently the most accurate and widely used algorithm for determining which TBI patients need a head CT scan. In the highest sensitivity (i.e. 100%), our method outperforms the Canadian rule in terms of specificity, accuracy and area under ROC curve using cost sensitive classifier. Clinical implementation of this algorithm can help decrease financial costs associated with Emergency Department evaluations for traumatic brain injury, while decreasing patient exposure to avoidable ionizing radiation.

Original languageEnglish (US)
Title of host publication2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2258-2261
Number of pages4
ISBN (Electronic)9781457702204
DOIs
StatePublished - Oct 13 2016
Event38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States
Duration: Aug 16 2016Aug 20 2016

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume2016-October
ISSN (Print)1557-170X

Other

Other38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
CountryUnited States
CityOrlando
Period8/16/168/20/16

Keywords

  • Canadian head CT rule
  • Classification
  • Head CT scan
  • Random forest
  • Traumatic brain injury

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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