  We propose the use of a conjecturing machine that suggests feature
relationships in the form of bounds involving nonlinear terms for numerical
features and boolean expressions for categorical features. The proposed
Conjecturing framework recovers known nonlinear and boolean relationships among
features from data. In both settings, true underlying relationships are
revealed. We then compare the method to a previously-proposed framework for
symbolic regression on the ability to recover equations that are satisfied
among features in a dataset. The framework is then applied to patient-level
data regarding COVID-19 outcomes to suggest possible risk factors that are
confirmed in the medical literature.
