Neural Survival Clustering: Non-parametric mixture of neural networks for survival clustering.
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Peer-reviewed
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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.
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Keywords
46 Information and Computing Sciences, 4611 Machine Learning, Neurological
Journal Title
Proc Mach Learn Res
Conference Name
Conference on Health, Inference, and Learning
Journal ISSN
2640-3498
2640-3498
2640-3498
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PMLR
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This work was supported by the UKRI Medical Research Council (MC_UU_00002/5 and MC_UU_00002/2).