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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Research works printed but not linked: 4
- 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
- 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.
- Quinlan, J. R. (1993). C 4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo.
- Rudin, C. and Ertekin, Ş. (2015). Learning optimized lists of classification rules. Technical report, MIT, Cambridge, MA.
Cited by
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"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Adadi, A., & Berrada, M. (2018) · IEEE Access · doi:10.1109/access.2018.2870052 · Cited by 6,294
“[66] proposed a model called Bayesian Rule Lists (BRL) based on decision tree, the authors claimed that preliminary interpretable models provide a concise and convincing capabilities to gain domain experts trust.”
BackgroundMethods for interpreting and understanding deep neural networks
Montavon, G., Samek, W., & Müller, K.-R. (2018) · Digital Signal Processing · doi:10.1016/j.dsp.2017.10.011 · Cited by 2,665
A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI
Tjoa, E., & Guan, C. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3027314 · Cited by 2,524
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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