Deep Phenotyping for Physiologic Biomarkers for Posttraumatic Epilepsy in Children

Abstract

PTE is a leading cause of acquired epilepsy, occurring in up to 20 percent of children following severe TBI and representing the leading cause of epilepsy in young adult adulthood. Increasing evidence suggests that the underlying physiologic environment immediately after TBI carries physiologic biomarkers for post-traumatic epileptogenesis. The goal of this project is to use advanced multivariate modeling to further our understanding of pediatric post-traumatic epileptogenesis. We proposed a statistical and data mining approach after pediatric severe TBI to identify physiologic biomarkers predictive of PTS and PTE. We will retrospectively explore our clinical database of high-frequency resolution multimodal neurologic monitoring data for predictive biomarkers of post-traumatic epilepsy, functional outcomes and post-traumatic seizures. We will also apply machine learning models towards predicting post-traumatic seizures.

Document Details

• Document Type: Technical Report
• Publication Date: Mar 01, 2021
• Accession Number: AD1125508

Entities

People

• Austin Jacobson
• Brian Appavu
• Hamy P. Temkit
• Stephen Foldes

Organizations

• Phoenix Children's Hospital

Tags

Communities of Interest

• Autonomy

DTIC Thesaurus Topics

• Biological Markers
• Biomedical Research
• Brain Injuries
• Data Analysis
• Data Management
• Data Mining
• Databases
• Epilepsy
• Governments
• Health Services
• Hemorrhage
• Hospitals
• Humanities
• Information Science
• Internet
• Local Governments
• Machine Learning
• Machines
• Medical Personnel
• Multivariate Analysis
• Neural Networks
• Patent Applications
• Students

Fields of Study

• Medicine

Readers

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• Neural Network Machine Learning.
• Neurotrauma and Rehabilitation Medicine.

Technology Areas

• AI & ML