Title

Heterogeneity in Blood Biomarker Trajectories after Mild TBI Revealed by Unsupervised Learning

Abstract

Concussions, also known as mild traumatic brain injury (mTBI), are a growing health challenge. Approximately four million concussions are diagnosed annually in the United States. Concussion is a heterogeneous disorder in causation, symptoms, and outcome making precision medicine approaches to this disorder important. Persistent disabling symptoms sometimes delay recovery in a difficult to predict subset of mTBI patients. Despite abundant data, clinicians need better tools to assess and predict recovery. Data-driven decision support holds promise for accurate clinical prediction tools for mTBI due to its ability to identify hidden correlations in complex datasets. We apply a Locality-Sensitive Hashing model enhanced by varied statistical methods to cluster blood biomarker level trajectories acquired over multiple time points. Additional features derived from demographics, injury context, neurocognitive assessment, and postural stability assessment are extracted using an autoencoder to augment the model. The data, obtained from FITBIR, consisted of 301 concussed subjects (athletes and cadets). Clustering identified 11 different biomarker trajectories. Two of the trajectories (rising GFAP and rising NF-L) were associated with a greater risk of loss of consciousness or post-traumatic amnesia at onset. The ability to cluster blood biomarker trajectories enhances the possibilities for precision medicine approaches to mTBI.

Department(s)

Electrical and Computer Engineering

Second Department

Mathematics and Statistics

Publication Status

Early Access

Comments

Research was sponsored by the Leonard Wood Institute in cooperation with the U.S. Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-14-2-0034.

Keywords and Phrases

Biological System Modeling; Blood; Concussions; GFAP; Injuries; NF-L; Precision Medicine; Predictive Modeling; Proteins; Sports; Statistical Analysis; Tau; Trajectory; UCH-L1; Unsupervised Learning

International Standard Serial Number (ISSN)

1557-9964; 1545-5963

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2021 Association for Computing Machinery (ACM), All rights reserved.

Publication Date

13 Jun 2021

Share

 
COinS