Understanding Coagulopathy using Multi-view Data in the Presence of Sub-Cohorts: A Hierarchical Subspace Approach

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Arya A. Pourzanjani, Tie Bo Wu, Richard M. Jiang, Mitchell J. Cohen, Linda R. Petzold ;
Proceedings of the 2nd Machine Learning for Healthcare Conference, PMLR 68:338-351, 2017.

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.

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