  Checklists are simple decision aids that are often used to promote safety and
reliability in clinical applications. In this paper, we present a method to
learn checklists for clinical decision support. We represent predictive
checklists as discrete linear classifiers with binary features and unit
weights. We then learn globally optimal predictive checklists from data by
solving an integer programming problem. Our method allows users to customize
checklists to obey complex constraints, including constraints to enforce group
fairness and to binarize real-valued features at training time. In addition, it
pairs models with an optimality gap that can inform model development and
determine the feasibility of learning sufficiently accurate checklists on a
given dataset. We pair our method with specialized techniques that speed up its
ability to train a predictive checklist that performs well and has a small
optimality gap. We benchmark the performance of our method on seven clinical
classification problems, and demonstrate its practical benefits by training a
short-form checklist for PTSD screening. Our results show that our method can
fit simple predictive checklists that perform well and that can easily be
customized to obey a rich class of custom constraints.

Machine Learning