Neural Survival Clustering: Non-parametric mixture of neural networks for survival clustering
Proceedings of Machine Learning Research
Conference on Health, Inference, and Learning
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Jeanselme, V., Tom, B., & Barrett, J. (2022). Neural Survival Clustering: Non-parametric mixture of neural networks for survival clustering. Proceedings of Machine Learning Research https://doi.org/10.17863/CAM.83437
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.
This work was supported by the UKRI Medical Research Council (MC UU 00002/5 and MC UU 00002/2).
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This record's DOI: https://doi.org/10.17863/CAM.83437
This record's URL: https://www.repository.cam.ac.uk/handle/1810/336005
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