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Evaluating Mental Health Encounters in mTBI: Identifying Patient Subgroups and Recommending Personalized Treatments

  • Filip Dabek
  • Peter Hoover
  • Jesus Caban
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11309)

Abstract

Mild Traumatic Brain Injuries (mTBIs) are “poorly understood” [6] and often associated with psychiatric conditions [21]. While machine learning techniques have explored these comorbidities, the utilization of psychiatric Electronic Health Records (EHRs) poses unique challenges, but provides great promise in the understanding of the brain and the effect of an mTBI [3, 14]. Therefore, in an effort to assist clinical practice in the field of mTBI, we present our work on utilizing EHR in which we apply machine learning models to identify and compare patient subgroups and explore algorithms to recommend patient catered treatment plans. Through this work, we aim to highlight effective techniques for handling the complexities of EHR and psychiatric-specific data.

Keywords

Traumatic brain injury Machine learning Mental health Psychiatry Healthcare application 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.National Intrepid Center of ExcellenceWalter Reed National Military Medical CenterBethesdaUSA

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