  Tabular datasets with low-sample-size or many variables are prevalent in
biomedicine. Practitioners in this domain prefer linear or tree-based models
over neural networks since the latter are harder to interpret and tend to
overfit when applied to tabular datasets. To address these neural networks'
shortcomings, we propose an intrinsically interpretable network for
heterogeneous biomedical data. We design a locally sparse neural network where
the local sparsity is learned to identify the subset of most relevant features
for each sample. This sample-specific sparsity is predicted via a
\textit{gating} network, which is trained in tandem with the
\textit{prediction} network. By forcing the model to select a subset of the
most informative features for each sample, we reduce model overfitting in
low-sample-size data and obtain an interpretable model. We demonstrate that our
method outperforms state-of-the-art models when applied to synthetic or
real-world biomedical datasets using extensive experiments. Furthermore, the
proposed framework dramatically outperforms existing schemes when evaluating
its interpretability capabilities. Finally, we demonstrate the applicability of
our model to two important biomedical tasks: survival analysis and marker gene
identification.
