  Electronic health records (EHRs) include simple features like patient age
together with more complex data like care history that are informative but not
easily represented as individual features. To better harness such data, we
developed an interpretable hybrid feature- and similarity-based model for
supervised learning that combines feature and kernel learning for prediction
and for investigation of causal relationships. We fit our hybrid models by
convex optimization with a sparsity-inducing penalty on the kernel. Depending
on the desired model interpretation, the feature and kernel coefficients can be
learned sequentially or simultaneously. The hybrid models showed comparable or
better predictive performance than solely feature- or similarity-based
approaches in a simulation study and in a case study to predict two-year risk
of loneliness or social isolation with EHR data from a complex primary health
care population. Using the case study we also present new kernels for
high-dimensional indicator-coded EHR data that are based on deviations from
population-level expectations, and we identify considerations for causal
interpretations.
