Dynamically Personalized Detection of Hemorrhage

Chirag Nagpal, Xinyu Li, Michael R. Pinsky, Artur Dubrawski
Proceedings of the 4th Machine Learning for Healthcare Conference, PMLR 106:109-123, 2019.

Abstract

Rapid detection of hemorrhage is of major interest to the critical care community, enabling clinicians to take swift actions to mitigate adverse outcomes. In this paper, we describe a model that allows rapid detection of the onset of hemorrhage by monitoring the Central Venous Pressure (CVP). As opposed to prior work in the domain, our model does not rely on prior availability of a stable physiology of a patient as a baseline of reference, and it makes generative assumptions on the monitored vital sign. This allows for rapid on-the-fly personalization to a previously unseen patient’s physiology. This property makes the proposed approach particularly relevant to e.g. trauma care and other scenarios where reference hemodynamic data may not be readily available for any new patient. We compare our model against strong discriminative alternatives and demonstrate its potential utility through empirical evaluation.

Cite this Paper

BibTeX
@InProceedings{pmlr-v106-nagpal19a, title = {Dynamically Personalized Detection of Hemorrhage}, author = {Nagpal, Chirag and Li, Xinyu and Pinsky, Michael R. and Dubrawski, Artur}, booktitle = {Proceedings of the 4th Machine Learning for Healthcare Conference}, pages = {109--123}, year = {2019}, editor = {Doshi-Velez, Finale and Fackler, Jim and Jung, Ken and Kale, David and Ranganath, Rajesh and Wallace, Byron and Wiens, Jenna}, volume = {106}, series = {Proceedings of Machine Learning Research}, month = {09--10 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v106/nagpal19a/nagpal19a.pdf}, url = {https://proceedings.mlr.press/v106/nagpal19a.html}, abstract = {Rapid detection of hemorrhage is of major interest to the critical care community, enabling clinicians to take swift actions to mitigate adverse outcomes. In this paper, we describe a model that allows rapid detection of the onset of hemorrhage by monitoring the Central Venous Pressure (CVP). As opposed to prior work in the domain, our model does not rely on prior availability of a stable physiology of a patient as a baseline of reference, and it makes generative assumptions on the monitored vital sign. This allows for rapid on-the-fly personalization to a previously unseen patient’s physiology. This property makes the proposed approach particularly relevant to e.g. trauma care and other scenarios where reference hemodynamic data may not be readily available for any new patient. We compare our model against strong discriminative alternatives and demonstrate its potential utility through empirical evaluation.} }
Endnote
%0 Conference Paper %T Dynamically Personalized Detection of Hemorrhage %A Chirag Nagpal %A Xinyu Li %A Michael R. Pinsky %A Artur Dubrawski %B Proceedings of the 4th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2019 %E Finale Doshi-Velez %E Jim Fackler %E Ken Jung %E David Kale %E Rajesh Ranganath %E Byron Wallace %E Jenna Wiens %F pmlr-v106-nagpal19a %I PMLR %P 109--123 %U https://proceedings.mlr.press/v106/nagpal19a.html %V 106 %X Rapid detection of hemorrhage is of major interest to the critical care community, enabling clinicians to take swift actions to mitigate adverse outcomes. In this paper, we describe a model that allows rapid detection of the onset of hemorrhage by monitoring the Central Venous Pressure (CVP). As opposed to prior work in the domain, our model does not rely on prior availability of a stable physiology of a patient as a baseline of reference, and it makes generative assumptions on the monitored vital sign. This allows for rapid on-the-fly personalization to a previously unseen patient’s physiology. This property makes the proposed approach particularly relevant to e.g. trauma care and other scenarios where reference hemodynamic data may not be readily available for any new patient. We compare our model against strong discriminative alternatives and demonstrate its potential utility through empirical evaluation.
APA
Nagpal, C., Li, X., Pinsky, M.R. & Dubrawski, A.. (2019). Dynamically Personalized Detection of Hemorrhage. Proceedings of the 4th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 106:109-123 Available from https://proceedings.mlr.press/v106/nagpal19a.html.

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