A bayesian approach to learning scoring systems

Big Data

Volumn 3, Issue 4, 2015, Pages 267-276

  Ertekin, Şeyda ^a,b   Rudin, Cynthia ^b  

^a MIDDLE EAST TECHNICAL UNIVERSITY   (Turkey)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

data mining; machine learning; predictive analytics

Indexed keywords

EID: 84991829574     PISSN: 21676461     EISSN: 2167647X     Source Type: Journal    
DOI: 10.1089/big.2015.0033     Document Type: Article

References (33)

1. Gage BF, Waterman AD, Shannon W, et al. Validation of clinical classification schemes for predicting stroke: results from the national registry of atrial fibrillation. Jama 2001; 285: 2864-2870.
2. Lip GY, Nieuwlaat R, Pisters R, et al. Refining clinical risk stratification for predicting stroke, and thromboembolism in atrial fibrillation using a novel risk factor-based approach: the Euro heart survey on atrial fibrillation. Chest 2010; 137: 263-272.
3. Wells PS, Anderson DR, Bormanis J, et al. Value of assessment of pretest probability of deep-vein thrombosis in clinical management. Lancet 1997; 350: 1795-1798.
4. Wells PS, Anderson DR, Rodger M, et al. Derivation of a simple clinical model to categorize patients probability of pulmonary embolismincreasing the models utility with the SimpliRED D-dimer. Thrombosis, and Haemostasis 2000; 83: 416-420.
5. Heap Analytics. Md+calc; 2015. Available online at: mdcalc.com (last accessed on Dec. 4, 2015
6. Knaus WA, Zimmerman JE, Wagner DP, et al. APACHE-Acute physiology, and chronic health evaluation: a physiologically based classification system. Critical Care Medicine 1981; 9: 591-597.
7. Antman EM, Cohen M, Bernink PJLM, et al. The TIMI risk score for unstable angina/non-ST elevation MI: a method for prognostication, and therapeutic decision making. Journal of the American Medical Association 2000; 284: 835-842.
8. Le Gall JR, Lemeshow S, Saulnier F. A new simplified acute physiology score (SAPS 13II) based on a European/North American multicenter study. The Journal of the American Medical Association 1993; 270: 2957-2963.
9. Freitas AA, Wieser DC, Apweiler R. On the importance of comprehensible classification models for protein function prediction. IEEE/ACM Trans Comput Biol Bioinformatics 2010; 7: 172-182.
10. Siddiqi N. Credit Risk Scorecards: Developing, and Implementing Intelligent Credit Scoring. Wiley, and SAS Business Series. New York: Wiley. 2005.
11. Martens D, Baesens B, Van Gestel T, and Vanthienen J. Comprehensible credit scoring models using rule extraction from support vector machines. European Journal of Operational Research 2007; 183: 1466-1476.
12. Martens D, Baesens B, Van Gestel T. Decompositional rule extraction from support vector machines by active learning. IEEE Trans on Knowl, and Data Eng 2009; 21: 178-191.
13. Verbeke W, Martens D, Mues C, Baesens B. Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert Systems with Applications 2011; 38: 2354-2364.
14. Ridgeway G. The pitfalls of prediction. NIJ Journal 2013; 271: 34-40.
15. Andrade JT. Handbook of violence risk assessment, and treatment: New approaches for mental health professionals. New York: Springer Publishing Company. 2009.
16. Giraud-Carrier C. Beyond predictive accuracy: what?. In: Proceedings of the ECML-98 Workshop on Upgrading Learning to Meta-Level: Model Selection, and Data Transformation, pages 78-85, 1998.
17. Le Gall JR, Loirat P, Alperovitch A, et al. A simplified acute physiology score for ICU patients. Critical Care Medicine 1984; 12: 975-977.
18. Moreno RP, Metnitz PGH, Almeida E, et al. SAPS 3 -from evaluation of the patient to evaluation of the intensive care unit. part 2: Development of a prognostic model for hospital mortality at ICU admission. Intensive Care Medicine 2005; 31: 1345-1355.
19. Knaus WA, Draper EA, Wagner DP, and Zimmerman JE. APACHE II: a severity of disease classification system. Critical Care Medicine 1985; 13: 818-829.
20. Knaus WA, Wagner DP, Draper EA, et al. The APACHE III prognostic system. risk prediction of hospital mortality for critically ill hospitalized adults. Chest Journal 1991; 100: 1619-1636.
21. Morrow DA, Antman EM, Charlesworth A, et al. TIMI risk score for STelevation myocardial infarction: a convenient, bedside, clinical score for risk assessment at presentation an intravenous nPA for treatment of infarcting myocardium early II trial substudy. Circulation 2000; 102: 2031-2037.
22. RC Bone, RA Balk, FB Cerra, et al. American college of chest physicians/.society of critical care medicine consensus conference: Definitions for sepsis, and organ failure, and guidelines for the use of innovative therapies in sepsis. Critical Care Medicine 1992; 20: 864-874.
23. Ranson JH, Rifkind KM, Roses DF, et al. Prognostic signs, and the role of operative management in acute pancreatitis. Surgery, Gynecology & Obstetrics 1974; 139: 69.
24. Light RW, Macgregor MI, Luchsinger PC, Ball WC. Pleural effusions: the diagnostic separation of transudates, and exudates. Annals of Internal Medicine 1972; 77: 507-513.
25. Chevaleyre Y, Koriche F, Zucker JD. Rounding methods for discrete linear classification. In: Proceedings of the 30th International Conference on Machine Learning (ICML-13), pages 651-659, 2013.
26. Carrizosa E, Nogales-Gómez A, Morales DR. Strongly agree or strongly disagree?: Rating features in support vector machines. Optimization Online, 2013. Available online at: www.optimization-online.org/.DB-FILE//2013/10/4082.pdf
27. Ustun B, Rudin C. Supersparse linear integer models for optimized medical scoring systems. Machine Learning, Online First: 1-43, November 2015.
28. Ustun B, Tracà S, Rudin C. Supersparse linear integer models for interpretable classification. 2013. Available online at: http://arxiv-web3.library.cornell.edu/pdf/1306.6677.pdf
29. Ustun B, Tracà S, Rudin C. Supersparse linear integer models for predictive scoring systems. In: Proceedings of AAAI Late Breaking Track, 2013.
30. Freitas AA. Comprehensible classification models: A position paper. SIGKDD Explorations Newsletter 2014; 15: 1-10.
31. Rü ping S. Learning interpretable models. PhD thesis, Universitä t Dortmund, 2006.
32. Bratko I. Machine learning: between accuracy, and interpretability. In: Della Riccia G, Lenz HJ, Kruse R, eds. Learning, Networks, and Statistics, volume 382, International Centre for Mechanical Sciences, Springer Vienna, 1997, pp 163-177.
33. Kodratoff Y. The comprehensibility manifesto. KDD Nugget Newsletter 1994; 94.