An interpretable stroke prediction model using rules and Bayesian analysis
Publication
, Conference
Letham, B; Rudin, C; McCormick, TH; Madigan, D
Published in: AAAI Workshop - Technical Report
January 1, 2013
We aim to produce predictive models that are not only accurate, but are also interpretable to human experts. We introduce a generative model called the Bayesian List Machine for fitting decision lists, a type of interpretable classifier, to data. We use the model to predict stroke in atrial fibrillation patients, and produce predictive models that are simple enough to be understood by patients yet significantly outperform the medical scoring systems currently in use.
Duke Scholars
Published In
AAAI Workshop - Technical Report
Publication Date
January 1, 2013
Volume
WS-13-17
Start / End Page
65 / 67
Citation
APA
Chicago
ICMJE
MLA
NLM
Letham, B., Rudin, C., McCormick, T. H., & Madigan, D. (2013). An interpretable stroke prediction model using rules and Bayesian analysis. In AAAI Workshop - Technical Report (Vol. WS-13-17, pp. 65–67).
Letham, B., C. Rudin, T. H. McCormick, and D. Madigan. “An interpretable stroke prediction model using rules and Bayesian analysis.” In AAAI Workshop - Technical Report, WS-13-17:65–67, 2013.
Letham B, Rudin C, McCormick TH, Madigan D. An interpretable stroke prediction model using rules and Bayesian analysis. In: AAAI Workshop - Technical Report. 2013. p. 65–7.
Letham, B., et al. “An interpretable stroke prediction model using rules and Bayesian analysis.” AAAI Workshop - Technical Report, vol. WS-13-17, 2013, pp. 65–67.
Letham B, Rudin C, McCormick TH, Madigan D. An interpretable stroke prediction model using rules and Bayesian analysis. AAAI Workshop - Technical Report. 2013. p. 65–67.
Published In
AAAI Workshop - Technical Report
Publication Date
January 1, 2013
Volume
WS-13-17
Start / End Page
65 / 67