DYNACARE: Dynamic Cardiac Arrest Risk Estimation

Joyce Ho, Yubin Park, Carlos Carvalho, Joydeep Ghosh
Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, PMLR 31:333-341, 2013.

Abstract

Cardiac arrest is a deadly condition caused by a sudden failure of the heart with an in-hospital mortality rate of ∼80%. Therefore, the ability to accurately estimate patients at high risk of cardiac arrest is crucial for improving the survival rate. Existing research generally fails to utilize a patient’s temporal dynamics. In this paper, we present two dynamic cardiac risk estimation models, focusing on different temporal signatures in a patient’s risk trajectory. These models can track a patient’s risk trajectory in real time, allow interpretability and predictability of a cardiac arrest event, provide an intuitive visualization to medical professionals, offer a personalized dynamic hazard function, and estimate the risk for a new patient.

Cite this Paper

BibTeX
@InProceedings{pmlr-v31-ho13b, title = {DYNACARE: Dynamic Cardiac Arrest Risk Estimation}, author = {Ho, Joyce and Park, Yubin and Carvalho, Carlos and Ghosh, Joydeep}, booktitle = {Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics}, pages = {333--341}, year = {2013}, editor = {Carvalho, Carlos M. and Ravikumar, Pradeep}, volume = {31}, series = {Proceedings of Machine Learning Research}, address = {Scottsdale, Arizona, USA}, month = {29 Apr--01 May}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v31/ho13b.pdf}, url = {https://proceedings.mlr.press/v31/ho13b.html}, abstract = {Cardiac arrest is a deadly condition caused by a sudden failure of the heart with an in-hospital mortality rate of ∼80%. Therefore, the ability to accurately estimate patients at high risk of cardiac arrest is crucial for improving the survival rate. Existing research generally fails to utilize a patient’s temporal dynamics. In this paper, we present two dynamic cardiac risk estimation models, focusing on different temporal signatures in a patient’s risk trajectory. These models can track a patient’s risk trajectory in real time, allow interpretability and predictability of a cardiac arrest event, provide an intuitive visualization to medical professionals, offer a personalized dynamic hazard function, and estimate the risk for a new patient.} }
Endnote
%0 Conference Paper %T DYNACARE: Dynamic Cardiac Arrest Risk Estimation %A Joyce Ho %A Yubin Park %A Carlos Carvalho %A Joydeep Ghosh %B Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2013 %E Carlos M. Carvalho %E Pradeep Ravikumar %F pmlr-v31-ho13b %I PMLR %P 333--341 %U https://proceedings.mlr.press/v31/ho13b.html %V 31 %X Cardiac arrest is a deadly condition caused by a sudden failure of the heart with an in-hospital mortality rate of ∼80%. Therefore, the ability to accurately estimate patients at high risk of cardiac arrest is crucial for improving the survival rate. Existing research generally fails to utilize a patient’s temporal dynamics. In this paper, we present two dynamic cardiac risk estimation models, focusing on different temporal signatures in a patient’s risk trajectory. These models can track a patient’s risk trajectory in real time, allow interpretability and predictability of a cardiac arrest event, provide an intuitive visualization to medical professionals, offer a personalized dynamic hazard function, and estimate the risk for a new patient.
RIS
TY - CPAPER TI - DYNACARE: Dynamic Cardiac Arrest Risk Estimation AU - Joyce Ho AU - Yubin Park AU - Carlos Carvalho AU - Joydeep Ghosh BT - Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics DA - 2013/04/29 ED - Carlos M. Carvalho ED - Pradeep Ravikumar ID - pmlr-v31-ho13b PB - PMLR DP - Proceedings of Machine Learning Research VL - 31 SP - 333 EP - 341 L1 - http://proceedings.mlr.press/v31/ho13b.pdf UR - https://proceedings.mlr.press/v31/ho13b.html AB - Cardiac arrest is a deadly condition caused by a sudden failure of the heart with an in-hospital mortality rate of ∼80%. Therefore, the ability to accurately estimate patients at high risk of cardiac arrest is crucial for improving the survival rate. Existing research generally fails to utilize a patient’s temporal dynamics. In this paper, we present two dynamic cardiac risk estimation models, focusing on different temporal signatures in a patient’s risk trajectory. These models can track a patient’s risk trajectory in real time, allow interpretability and predictability of a cardiac arrest event, provide an intuitive visualization to medical professionals, offer a personalized dynamic hazard function, and estimate the risk for a new patient. ER -
APA
Ho, J., Park, Y., Carvalho, C. & Ghosh, J.. (2013). DYNACARE: Dynamic Cardiac Arrest Risk Estimation. Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 31:333-341 Available from https://proceedings.mlr.press/v31/ho13b.html.

Related Material