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arXiv · 1812.00030

Unsupervised learning with GLRM feature selection reveals novel traumatic brain injury phenotypes

Aaron J. Masino·Kaitlin A. Folweiler
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Abstract

Baseline injury categorization is important to traumatic brain injury (TBI) research and treatment. Current categorization is dominated by symptom-based scores that insufficiently capture injury heterogeneity. In this work, we apply unsupervised clustering to identify novel TBI phenotypes. Our approach uses a generalized low-rank model (GLRM) model for feature selection in a procedure analogous to wrapper methods. The resulting clusters reveal four novel TBI phenotypes with distinct feature profiles and that correlate to 90-day functional and cognitive status.

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BibTeXRIS

Aaron J. Masino, Kaitlin A. Folweiler. 2018-11-30. Unsupervised learning with GLRM feature selection reveals novel traumatic brain injury phenotypes. https://arxiv.org/abs/1812.00030

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