Understanding Coagulopathy Using Multi-View Data in the Presence of Sub-Cohorts: A Hierarchical Subspace Approach
Abstract
Death from trauma is most often the result of uncontrollable bleeding as a result of Acute Traumatic Coagulopathy (ATC), a disease that manifests itself differently in different sub-cohorts of trauma patients. Understanding the mechanisms of ATC and how existing patient tests can inform us about these mechanisms is key to treating the disease. We introduce a hierarchical Canonical Correlation Analysis (CCA) model that captures a lower dimensional representation of the coagulation system based on blood protein and other tests. The hierarchial nature of the model is ideal in the setting where multiple sub-cohorts are present, but statistical strength can reasonably be borrowed from similar groups. We illustrate how the model may be useful in understanding and treating ATC.
Cite
Text
Pourzanjani et al. "Understanding Coagulopathy Using Multi-View Data in the Presence of Sub-Cohorts: A Hierarchical Subspace Approach." Proceedings of the 2nd Machine Learning for Healthcare Conference, 2017.Markdown
[Pourzanjani et al. "Understanding Coagulopathy Using Multi-View Data in the Presence of Sub-Cohorts: A Hierarchical Subspace Approach." Proceedings of the 2nd Machine Learning for Healthcare Conference, 2017.](https://mlanthology.org/mlhc/2017/pourzanjani2017mlhc-understanding/)BibTeX
@inproceedings{pourzanjani2017mlhc-understanding,
title = {{Understanding Coagulopathy Using Multi-View Data in the Presence of Sub-Cohorts: A Hierarchical Subspace Approach}},
author = {Pourzanjani, Arya A. and Wu, Tie Bo and Jiang, Richard M. and Cohen, Mitchell J. and Petzold, Linda R.},
booktitle = {Proceedings of the 2nd Machine Learning for Healthcare Conference},
year = {2017},
pages = {338-351},
volume = {68},
url = {https://mlanthology.org/mlhc/2017/pourzanjani2017mlhc-understanding/}
}