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Poster
Learning Optimal Predictive Checklists
Haoran Zhang · Quaid Morris · Berk Ustun · Marzyeh Ghassemi

Thu Dec 09 04:30 PM -- 06:00 PM (PST) @
in Poster Session 7 »

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.

Keywords: Optimization · Machine Learning · Fairness · Interpretability

Author Information

Haoran Zhang (Massachusetts Institute of Technology)
Quaid Morris (Memorial Sloan Kettering)
Berk Ustun (Harvard University)
Marzyeh Ghassemi (University of Toronto, Vector Institute)

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