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
Open accessAbstract
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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“To this end, they propose Bayesian Rule Lists (BRL) f to extract the comprehensible global predictor c g as a decision list . A decision list consists of a series of if-then statements discretizing the whole feature space into a set of simple and directly interpretable decision statements.”
From A Survey of Methods for Explaining Black Box Models (2019) · cites this paper as [57]
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- 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
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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)
- 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
- 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
- bronze
- doi.org
- 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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