  Clinical prognostic models derived from largescale healthcare data can inform
critical diagnostic and therapeutic decisions. To enable off-theshelf usage of
machine learning (ML) in prognostic research, we developed AUTOPROGNOSIS: a
system for automating the design of predictive modeling pipelines tailored for
clinical prognosis. AUTOPROGNOSIS optimizes ensembles of pipeline
configurations efficiently using a novel batched Bayesian optimization (BO)
algorithm that learns a low-dimensional decomposition of the pipelines
high-dimensional hyperparameter space in concurrence with the BO procedure.
This is achieved by modeling the pipelines performances as a black-box function
with a Gaussian process prior, and modeling the similarities between the
pipelines baseline algorithms via a sparse additive kernel with a Dirichlet
prior. Meta-learning is used to warmstart BO with external data from similar
patient cohorts by calibrating the priors using an algorithm that mimics the
empirical Bayes method. The system automatically explains its predictions by
presenting the clinicians with logical association rules that link patients
features to predicted risk strata. We demonstrate the utility of AUTOPROGNOSIS
using 10 major patient cohorts representing various aspects of cardiovascular
patient care.
