Heterogeneity in Blood Biomarker Trajectories after Mild TBI Revealed by Unsupervised Learning
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.
L. A. Bui et al., "Heterogeneity in Blood Biomarker Trajectories after Mild TBI Revealed by Unsupervised Learning," IEEE/ACM Transactions on Computational Biology and Bioinformatics, Association for Computing Machinery (ACM), Jun 2021.
The definitive version is available at https://doi.org/10.1109/TCBB.2021.3091972
Electrical and Computer Engineering
Mathematics and Statistics
Intelligent Systems Center
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)
Article - Journal
© 2021 Association for Computing Machinery (ACM), All rights reserved.
13 Jun 2021