Date of Award


Document Type


Degree Name

Master of Science


Department of Operational Sciences

First Advisor

Nathan B. Gaw, PhD


A post-traumatic headache (PTH), resulting from a mild traumatic brain injury (mTBI), potentially develops into persistent post-traumatic headache (PPTH). Although no known cure for PPTH exists, research has shown that receiving treatment at earlier stages of PTH lowers the risk of patients developing PPTH. Previous studies have shown machine learning (ML) models capable of predicting a patient’s PTH progression, but none have considered the issue of protecting patient privacy. Due to patient privacy, ML models only have access to data within the institution. Federated learning (FL) harnesses data from separate institutions without sacrificing patient privacy as institutions can run ML models on their own private dataset and share the trained model parameters without sharing data. Additionally, quantifying uncertainty of model parameters associated with key features of interest in predicting PTH progression has not been explored in the context of FL. Uncertainty Quantification in Federated Learning (UQFL) combines FL and uncertainty quantification to protect patient privacy and provide a measure of uncertainty for each model parameter.

AFIT Designator



A 12-month embargo was observed.

Approved for public release. Case number on file.