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

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v68-pourzanjani17a, 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}, pages = {338--351}, year = {2017}, editor = {Doshi-Velez, Finale and Fackler, Jim and Kale, David and Ranganath, Rajesh and Wallace, Byron and Wiens, Jenna}, volume = {68}, series = {Proceedings of Machine Learning Research}, month = {18--19 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v68/pourzanjani17a/pourzanjani17a.pdf}, url = {https://proceedings.mlr.press/v68/pourzanjani17a.html}, 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.} }
Endnote
%0 Conference Paper %T Understanding Coagulopathy using Multi-view Data in the Presence of Sub-Cohorts: A Hierarchical Subspace Approach %A Arya A. Pourzanjani %A Tie Bo Wu %A Richard M. Jiang %A Mitchell J. Cohen %A Linda R. Petzold %B Proceedings of the 2nd Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2017 %E Finale Doshi-Velez %E Jim Fackler %E David Kale %E Rajesh Ranganath %E Byron Wallace %E Jenna Wiens %F pmlr-v68-pourzanjani17a %I PMLR %P 338--351 %U https://proceedings.mlr.press/v68/pourzanjani17a.html %V 68 %X 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.
APA
Pourzanjani, A.A., Wu, T.B., Jiang, R.M., Cohen, M.J. & Petzold, L.R.. (2017). 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, in Proceedings of Machine Learning Research 68:338-351 Available from https://proceedings.mlr.press/v68/pourzanjani17a.html.

Related Material