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Cited by 766Explainable Artificial Intelligence (XAI)Artificial Intelligence in HealthcareMachine Learning in Healthcare

The DOI matches its Crossref record; 73 of the 75 references it cites were found in a registry or an index.

Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model

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Letham, Benjamin, Rudin, Cynthia, McCormick, Tyler H., Madigan, David·2015·The Annals of Applied Statistics
DOI 10.1214/15-aoas848
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Abstract

We aim to produce predictive models that are not only accurate, but are also interpretable to human experts. Our models are decision lists, which consist of a series of if…then…statements (e.g., if high blood pressure, then stroke) that discretize a high-dimensional, multivariate feature space into a series of simple, readily interpretable decision statements. We introduce a generative model called Bayesian Rule Lists that yields a posterior distribution over possible decision lists. It employs a novel prior structure to encourage sparsity. Our experiments show that Bayesian Rule Lists has predictive accuracy on par with the current top algorithms for prediction in machine learning. Our method is motivated by recent developments in personalized medicine, and can be used to produce highly accurate and interpretable medical scoring systems. We demonstrate this by producing an alternative to the CHADS$_{2}$ score, actively used in clinical practice for estimating the risk of stroke in patients that have atrial fibrillation. Our model is as interpretable as CHADS$_{2}$, but more accurate.

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Research works printed but not linked: 4

  1. Letham, B., Rudin, C., McCormick, T. H. and Madigan, D. (2015). Supplement to “Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model.” DOI:10.1214/15-AOAS848SUPPA, DOI:10.1214/15-AOAS848SUPPB. · DOI
  2. Letham, B., Rudin, C., McCormick, T. H. and Madigan, D. (2014). An interpretable model for stroke prediction using rules and Bayesian analysis. In Proceedings of 2014 KDD Workshop on Data Science for Social Good. MIT, Cambridge, MA.
  3. Quinlan, J. R. (1993). C 4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo.
  4. Rudin, C. and Ertekin, Ş. (2015). Learning optimized lists of classification rules. Technical report, MIT, Cambridge, MA.

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Metadata

Authors
Letham, B., Rudin, C., McCormick, T. H., & Madigan, D.
Affiliations
  • Massachusetts Institute of Technology (US)
  • University of Washington (US)
ORCID
Venue
The Annals of Applied Statistics
Publisher
Institute of Mathematical Statistics
Published
2015-09-01
Volume
9
Issue
3
Pages
1350-1371
ISSN
  • 1932-6157 (ISSN-L)
  • 1941-7330
DOI
10.1214/15-aoas848
OpenAlex
W2118022153
Cited by
766
References
75
Citations by year
2026: 36 · 2025: 51 · 2024: 77 · 2023: 91 · 2022: 90 · 2021: 108 · 2020: 76 · 2019: 88 · 2018: 65 · 2017: 42 · 2016: 26 · 2015: 14
FWCI
47.96
Type
journal article
Language
en
Open access
Locations
  • The Annals of Applied Statistics (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Project Euclid (Cornell University) (submittedVersion)
  • DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
  • arXiv (Cornell University)
Funders
  • National Science Foundation (1053407, CAREER)
  • National Institutes of Health
  • National Institute of Allergy and Infectious Diseases
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords
interpretable classifiers, decision lists, generative models, sparsity prior, stroke risk prediction, atrial fibrillation, CHADS2 score, personalized medicine, rule-based prediction

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