Comparison of Multi-class Machine Learning Methods for the Identification of Factors Most Predictive of Prognosis in Neurobehavioral assessment of Pediatric Severe Disorder of Consciousness through LOCFAS scale

Erika Molteni*, Katia Colombo, Elena Beretta, Susanna Galbiati, Liane Dos Santos Canas, Marc Modat, Sandra Strazzer

*Corresponding author for this work

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

4 Citations (Scopus)

Abstract

Severe Disorders of Consciousness (DoC) are generally caused by brain trauma, anoxia or stroke, and result in conditions ranging from coma to the confused-agitated state. Prognostic decision is difficult to achieve during the first year after injury, especially in the pediatric cases. Nevertheless, prognosis crucially informs rehabilitation decision and family expectations. We compared four multi-class machine learning classification approaches for the prognostic decision in pediatric DoC. We identified domains of a neurobehavioral assessment tool, Level of Cognitive Functioning Assessment Scale, mostly contributing to decision in a cohort of 124 cases. We showed the possibility to generalize to new admitted pediatric cases, thus paving the way for real employment of machine learning classifiers as an assistive tool to prognostic decision in clinics.

Original languageEnglish
Title of host publication2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages269-272
Number of pages4
Volume2019
ISBN (Electronic)9781538613115
DOIs
Publication statusPublished - Jul 2019
Event41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 - Berlin, Germany
Duration: 23 Jul 201927 Jul 2019

Publication series

NameConference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISSN (Print)1557-170X

Conference

Conference41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Country/TerritoryGermany
CityBerlin
Period23/07/201927/07/2019

Keywords

  • Child
  • Coma
  • Consciousness
  • Consciousness Disorders
  • Humans
  • Machine Learning
  • Prognosis

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