Optimized risk scores

Published

Conference Paper

© 2017 Copyright held by the owner/author(s). Risk scores are simple classification models that let users quickly assess risk by adding, subtracting, and multiplying a few small numbers. Such models are widely used in healthcare and criminal justice, but are often built ad hoc. In this paper, we present a principled approach to learn risk scores that are fully optimized for feature selection, integer coefficients, and operational constraints. We formulate the risk score problem as a mixed integer nonlinear program, and present a new cutting plane algorithm to efficiently recover its optimal solution. Our approach can fit optimized risk scores in a way that scales linearly with the sample size of a dataset, provides a proof of optimality, and obeys complex constraints without parameter tuning. We illustrate these benefits through an extensive set of numerical experiments, and an application where we build a customized risk score for ICU seizure prediction.

Full Text

Duke Authors

Cited Authors

  • Ustun, B; Rudin, C

Published Date

  • August 13, 2017

Published In

  • Proceedings of the Acm Sigkdd International Conference on Knowledge Discovery and Data Mining

Volume / Issue

  • Part F129685 /

Start / End Page

  • 1125 - 1134

International Standard Book Number 13 (ISBN-13)

  • 9781450348874

Digital Object Identifier (DOI)

  • 10.1145/3097983.3098161

Citation Source

  • Scopus