Deep Survival Analysis

Rajesh Ranganath, Adler Perotte, Noémie Elhadad, David Blei
Proceedings of the 1st Machine Learning for Healthcare Conference, PMLR 56:101-114, 2016.

Abstract

The electronic health record (EHR) provides an unprecedented opportunity to build actionable tools to support physicians at the point of care. In this paper, we introduce deep survival analysis, a hierarchical generative approach to survival analysis in the context of the EHR. It departs from previous approaches in two main ways: (1) all observations, including covariates, are modeled jointly conditioned on a rich latent structure; and (2) the observations are aligned by their failure time, rather than by an arbitrary time zero as in traditional survival analysis. Further, it handles heterogeneous data types that occur in the EHR. We validate deep survival analysis by stratifying patients according to risk of developing coronary heart disease (CHD) on 313,000 patients corresponding to 5.5 million months of observations. When compared to the clinically validated Framingham CHD risk score, deep survival analysis is superior in stratifying patients according to their risk.

Cite this Paper

BibTeX
@InProceedings{pmlr-v56-Ranganath16, title = {Deep Survival Analysis}, author = {Ranganath, Rajesh and Perotte, Adler and Elhadad, Noémie and Blei, David}, booktitle = {Proceedings of the 1st Machine Learning for Healthcare Conference}, pages = {101--114}, year = {2016}, editor = {Doshi-Velez, Finale and Fackler, Jim and Kale, David and Wallace, Byron and Wiens, Jenna}, volume = {56}, series = {Proceedings of Machine Learning Research}, address = {Northeastern University, Boston, MA, USA}, month = {18--19 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v56/Ranganath16.pdf}, url = {https://proceedings.mlr.press/v56/Ranganath16.html}, abstract = {The electronic health record (EHR) provides an unprecedented opportunity to build actionable tools to support physicians at the point of care. In this paper, we introduce deep survival analysis, a hierarchical generative approach to survival analysis in the context of the EHR. It departs from previous approaches in two main ways: (1) all observations, including covariates, are modeled jointly conditioned on a rich latent structure; and (2) the observations are aligned by their failure time, rather than by an arbitrary time zero as in traditional survival analysis. Further, it handles heterogeneous data types that occur in the EHR. We validate deep survival analysis by stratifying patients according to risk of developing coronary heart disease (CHD) on 313,000 patients corresponding to 5.5 million months of observations. When compared to the clinically validated Framingham CHD risk score, deep survival analysis is superior in stratifying patients according to their risk.} }
Endnote
%0 Conference Paper %T Deep Survival Analysis %A Rajesh Ranganath %A Adler Perotte %A Noémie Elhadad %A David Blei %B Proceedings of the 1st Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2016 %E Finale Doshi-Velez %E Jim Fackler %E David Kale %E Byron Wallace %E Jenna Wiens %F pmlr-v56-Ranganath16 %I PMLR %P 101--114 %U https://proceedings.mlr.press/v56/Ranganath16.html %V 56 %X The electronic health record (EHR) provides an unprecedented opportunity to build actionable tools to support physicians at the point of care. In this paper, we introduce deep survival analysis, a hierarchical generative approach to survival analysis in the context of the EHR. It departs from previous approaches in two main ways: (1) all observations, including covariates, are modeled jointly conditioned on a rich latent structure; and (2) the observations are aligned by their failure time, rather than by an arbitrary time zero as in traditional survival analysis. Further, it handles heterogeneous data types that occur in the EHR. We validate deep survival analysis by stratifying patients according to risk of developing coronary heart disease (CHD) on 313,000 patients corresponding to 5.5 million months of observations. When compared to the clinically validated Framingham CHD risk score, deep survival analysis is superior in stratifying patients according to their risk.
RIS
TY - CPAPER TI - Deep Survival Analysis AU - Rajesh Ranganath AU - Adler Perotte AU - Noémie Elhadad AU - David Blei BT - Proceedings of the 1st Machine Learning for Healthcare Conference DA - 2016/12/10 ED - Finale Doshi-Velez ED - Jim Fackler ED - David Kale ED - Byron Wallace ED - Jenna Wiens ID - pmlr-v56-Ranganath16 PB - PMLR DP - Proceedings of Machine Learning Research VL - 56 SP - 101 EP - 114 L1 - http://proceedings.mlr.press/v56/Ranganath16.pdf UR - https://proceedings.mlr.press/v56/Ranganath16.html AB - The electronic health record (EHR) provides an unprecedented opportunity to build actionable tools to support physicians at the point of care. In this paper, we introduce deep survival analysis, a hierarchical generative approach to survival analysis in the context of the EHR. It departs from previous approaches in two main ways: (1) all observations, including covariates, are modeled jointly conditioned on a rich latent structure; and (2) the observations are aligned by their failure time, rather than by an arbitrary time zero as in traditional survival analysis. Further, it handles heterogeneous data types that occur in the EHR. We validate deep survival analysis by stratifying patients according to risk of developing coronary heart disease (CHD) on 313,000 patients corresponding to 5.5 million months of observations. When compared to the clinically validated Framingham CHD risk score, deep survival analysis is superior in stratifying patients according to their risk. ER -
APA
Ranganath, R., Perotte, A., Elhadad, N. & Blei, D.. (2016). Deep Survival Analysis. Proceedings of the 1st Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 56:101-114 Available from https://proceedings.mlr.press/v56/Ranganath16.html.

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