Neural Survival Clustering: Non-parametric mixture of neural networks for survival clustering

Vincent Jeanselme, Brian Tom, Jessica Barrett
Proceedings of the Conference on Health, Inference, and Learning, PMLR 174:92-102, 2022.

Abstract

Survival analysis involves the modelling of the times to event. Proposed neural network approaches maximise the predictive performance of traditional survival models at the cost of their interpretability. This impairs their applicability in high stake domains such as medicine. Providing insights into the survival distributions would tackle this issue and advance the medical understanding of diseases. This paper approaches survival analysis as a mixture of neural baselines whereby different baseline cumulative hazard functions are modelled using positive and monotone neural networks. The efficiency of the solution is demonstrated on three datasets while enabling the discovery of new survival phenotypes.

Cite this Paper

BibTeX
@InProceedings{pmlr-v174-jeanselme22a, title = {Neural Survival Clustering: Non-parametric mixture of neural networks for survival clustering}, author = {Jeanselme, Vincent and Tom, Brian and Barrett, Jessica}, booktitle = {Proceedings of the Conference on Health, Inference, and Learning}, pages = {92--102}, year = {2022}, editor = {Flores, Gerardo and Chen, George H and Pollard, Tom and Ho, Joyce C and Naumann, Tristan}, volume = {174}, series = {Proceedings of Machine Learning Research}, month = {07--08 Apr}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v174/jeanselme22a/jeanselme22a.pdf}, url = {https://proceedings.mlr.press/v174/jeanselme22a.html}, abstract = {Survival analysis involves the modelling of the times to event. Proposed neural network approaches maximise the predictive performance of traditional survival models at the cost of their interpretability. This impairs their applicability in high stake domains such as medicine. Providing insights into the survival distributions would tackle this issue and advance the medical understanding of diseases. This paper approaches survival analysis as a mixture of neural baselines whereby different baseline cumulative hazard functions are modelled using positive and monotone neural networks. The efficiency of the solution is demonstrated on three datasets while enabling the discovery of new survival phenotypes.} }
Endnote
%0 Conference Paper %T Neural Survival Clustering: Non-parametric mixture of neural networks for survival clustering %A Vincent Jeanselme %A Brian Tom %A Jessica Barrett %B Proceedings of the Conference on Health, Inference, and Learning %C Proceedings of Machine Learning Research %D 2022 %E Gerardo Flores %E George H Chen %E Tom Pollard %E Joyce C Ho %E Tristan Naumann %F pmlr-v174-jeanselme22a %I PMLR %P 92--102 %U https://proceedings.mlr.press/v174/jeanselme22a.html %V 174 %X Survival analysis involves the modelling of the times to event. Proposed neural network approaches maximise the predictive performance of traditional survival models at the cost of their interpretability. This impairs their applicability in high stake domains such as medicine. Providing insights into the survival distributions would tackle this issue and advance the medical understanding of diseases. This paper approaches survival analysis as a mixture of neural baselines whereby different baseline cumulative hazard functions are modelled using positive and monotone neural networks. The efficiency of the solution is demonstrated on three datasets while enabling the discovery of new survival phenotypes.
APA
Jeanselme, V., Tom, B. & Barrett, J.. (2022). Neural Survival Clustering: Non-parametric mixture of neural networks for survival clustering. Proceedings of the Conference on Health, Inference, and Learning, in Proceedings of Machine Learning Research 174:92-102 Available from https://proceedings.mlr.press/v174/jeanselme22a.html.

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