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Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model
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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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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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