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

Xiaobin Shen, George H. Chen

Keywords: survival analysis, competing risks, neural networks, interpretability

Track: Proceedings

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 (CIFs). 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 CIFs 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.

General Area: Models and Methods

Specific Subject Areas: Survival Analysis, Explainability & Interpretability

Data And Code Availability: Yes

Ethics Board Approval: No

Entered Conflicts: I confirm the above

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Submission Number: 167