Deep Kernel Aalen-Johansen Estimator: An Interpretable and Flexible Neural Net Framework for Competing Risks

Xiaobin Shen, George H. Chen
Proceedings of the Fifth Machine Learning for Health Symposium, PMLR 297:1096-1125, 2026.

Abstract

We propose an interpretable deep competing risks model called the Deep Kernel Aalen-Johansen ({DKAJ}) estimator, which generalizes the classical Aalen-Johansen nonparametric estimate of cumulative incidence functions ({CIF}s). Each data point (e.g., patient) is represented as a weighted combination of clusters. If a data point has nonzero weight only for one cluster, then its predicted {CIF}s correspond to those of the classical Aalen-Johansen estimator restricted to data points from that cluster. These weights come from an automatically learned kernel function that measures how similar any two data points are. On four standard competing risks datasets, we show that {DKAJ} is competitive with state-of-the-art baselines while being able to provide visualizations to assist model interpretation.

Cite this Paper

BibTeX
@InProceedings{pmlr-v297-shen26a, title = {Deep Kernel Aalen-Johansen Estimator: An Interpretable and Flexible Neural Net Framework for Competing Risks}, author = {Shen, Xiaobin and Chen, George H.}, booktitle = {Proceedings of the Fifth Machine Learning for Health Symposium}, pages = {1096--1125}, year = {2026}, editor = {Argaw, Peniel and Zhang, Haoran and Jabbour, Sarah and Chandak, Payal and Ji, Jerry and Mukherjee, Sumit and Salaudeen, Olawale and Chang, Trenton and Healey, Elizabeth and Gröger, Fabian and Adibi, Amin and Hegselmann, Stefan and Wild, Benjamin and Noori, Ayush}, volume = {297}, series = {Proceedings of Machine Learning Research}, month = {13--14 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v297/main/assets/shen26a/shen26a.pdf}, url = {https://proceedings.mlr.press/v297/shen26a.html}, abstract = {We propose an interpretable deep competing risks model called the Deep Kernel Aalen-Johansen ({DKAJ}) estimator, which generalizes the classical Aalen-Johansen nonparametric estimate of cumulative incidence functions ({CIF}s). Each data point (e.g., patient) is represented as a weighted combination of clusters. If a data point has nonzero weight only for one cluster, then its predicted {CIF}s correspond to those of the classical Aalen-Johansen estimator restricted to data points from that cluster. These weights come from an automatically learned kernel function that measures how similar any two data points are. On four standard competing risks datasets, we show that {DKAJ} is competitive with state-of-the-art baselines while being able to provide visualizations to assist model interpretation.} }
Endnote
%0 Conference Paper %T Deep Kernel Aalen-Johansen Estimator: An Interpretable and Flexible Neural Net Framework for Competing Risks %A Xiaobin Shen %A George H. Chen %B Proceedings of the Fifth Machine Learning for Health Symposium %C Proceedings of Machine Learning Research %D 2026 %E Peniel Argaw %E Haoran Zhang %E Sarah Jabbour %E Payal Chandak %E Jerry Ji %E Sumit Mukherjee %E Olawale Salaudeen %E Trenton Chang %E Elizabeth Healey %E Fabian Gröger %E Amin Adibi %E Stefan Hegselmann %E Benjamin Wild %E Ayush Noori %F pmlr-v297-shen26a %I PMLR %P 1096--1125 %U https://proceedings.mlr.press/v297/shen26a.html %V 297 %X We propose an interpretable deep competing risks model called the Deep Kernel Aalen-Johansen ({DKAJ}) estimator, which generalizes the classical Aalen-Johansen nonparametric estimate of cumulative incidence functions ({CIF}s). Each data point (e.g., patient) is represented as a weighted combination of clusters. If a data point has nonzero weight only for one cluster, then its predicted {CIF}s correspond to those of the classical Aalen-Johansen estimator restricted to data points from that cluster. These weights come from an automatically learned kernel function that measures how similar any two data points are. On four standard competing risks datasets, we show that {DKAJ} is competitive with state-of-the-art baselines while being able to provide visualizations to assist model interpretation.
APA
Shen, X. & Chen, G.H.. (2026). Deep Kernel Aalen-Johansen Estimator: An Interpretable and Flexible Neural Net Framework for Competing Risks. Proceedings of the Fifth Machine Learning for Health Symposium, in Proceedings of Machine Learning Research 297:1096-1125 Available from https://proceedings.mlr.press/v297/shen26a.html.

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