  Risk scores are widely used for clinical decision making and commonly
generated from logistic regression models. Machine-learning-based methods may
work well for identifying important predictors, but such 'black box' variable
selection limits interpretability, and variable importance evaluated from a
single model can be biased. We propose a robust and interpretable variable
selection approach using the recently developed Shapley variable importance
cloud (ShapleyVIC) that accounts for variability across models. Our approach
evaluates and visualizes overall variable contributions for in-depth inference
and transparent variable selection, and filters out non-significant
contributors to simplify model building steps. We derive an ensemble variable
ranking from variable contributions, which is easily integrated with an
automated and modularized risk score generator, AutoScore, for convenient
implementation. In a study of early death or unplanned readmission, ShapleyVIC
selected 6 of 41 candidate variables to create a well-performing model, which
had similar performance to a 16-variable model from machine-learning-based
ranking.
