An interpretable neural network for outcome prediction in traumatic brain injury.

Journal: BMC medical informatics and decision making
Published Date: Aug 1, 2022

Abstract

BACKGROUND: Traumatic Brain Injury (TBI) is a common condition with potentially severe long-term complications, the prediction of which remains challenging. Machine learning (ML) methods have been used previously to help physicians predict long-term outcomes of TBI so that appropriate treatment plans can be adopted. However, many ML techniques are "black box": it is difficult for humans to understand the decisions made by the model, with post-hoc explanations only identifying isolated relevant factors rather than combinations of factors. Moreover, such models often rely on many variables, some of which might not be available at the time of hospitalization.

Authors

  • Cristian Minoccheri
    Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, USA. minoc@umich.edu.
  • Craig A Williamson
    Department of Neurosurgery, University of Michigan, Ann Arbor, USA.
  • Mark Hemmila
    Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, USA.
  • Kevin Ward
  • Erica B Stein
    Department of Radiology, Michigan Medicine, Ann Arbor, MI, 48109, USA.
  • Jonathan Gryak
  • Kayvan Najarian

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