Unsupervised learning with GLRM feature selection reveals novel traumatic brain injury phenotypes
- Resource Type
- Working Paper
- Authors
- Masino, Aaron J.; Folweiler, Kaitlin A.
- Source
- Subject
- Computer Science - Machine Learning
Quantitative Biology - Quantitative Methods
Statistics - Machine Learning
- Language
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.
Comment: Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.07216