Machine learning for personalized medicine: Predicting primary myocardial infarction from electronic health records

AI Magazine

Volumn 33, Issue 4, 2012, Pages 33-45

  Weiss, Jeremy C ^a   Natarajan, Sriraam ^a,b   Peissig, Peggy L ^c   McCarty, Catherine A ^d   Page, David ^a,e  

^a UNIVERSITY OF WISCONSIN MADISON   (United States)

^b WAKE FOREST SCHOOL OF MEDICINE   (United States)

^c MARSHFIELD CLINIC   (United States)

^d ESSENTIA INSTITUTE OF RURAL HEALTH   (United States)

^e UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CARDIOLOGY; E-LEARNING; FORECASTING; RECORDS MANAGEMENT;

CLINICAL OUTCOME; ELECTRONIC HEALTH RECORD; ELECTRONIC HEALTH RECORD (EHRS); FUNCTIONAL GRADIENT; MYOCARDIAL INFARCTION; PERSONALIZED MEDICINES; STATISTICAL RELATIONAL LEARNING;

LEARNING SYSTEMS;

EID: 84876193099     PISSN: 07384602     EISSN: None     Source Type: Journal    
DOI: 10.1609/aimag.v33i4.2438     Document Type: Article

References (28)

1. Anderson, G., and Pfahringer, B. 2009. Relational Random Forests Based on Random Relational Rules. In Proceedings of the Twenty-First International Joint Conferences in Artificial Intelligence (IJCAI). Menlo Park, CA: AAAI Press.
2. Antonopoulos, S. 2002. Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) Final Report. Circulation 106(3143): 3421.
3. Bg-Hansen, E.; Larsson, C. A.; Gullberg, B.; Melander, A.; Bostrm, K.; Rstam, L.; and Lindblad, U. 2007. Predictors of Acute Myocardial Infarction Mortality in Hypertensive Patients Treated in Primary Care. Scandinavian Journal of Primary Health Care 25(4): 237-243.
4. Blockeel, H., and Raedt, L. D. 1998. Top-Down Induction of First-Order Logical Decision Trees. Artificial Intelligence 101(1-2): 285-297.
5. Craven, M., and Shavlik, J. 1996. Extracting Tree-Structured Representations of Trained Networks. In Advances of the Neural Information Processing Systems (NIPS) Conference, 24-30. Cambridge, MA: The MIT Press.
6. Damani, S.; Bacconi, A.; Libiger, O.; Chourasia, A. H.; Ser-ry, R.; Gollapudi, R.; Goldberg, R.; Rapeport, K.; Haaser, S.; and Topol, S. 2012. Characterization of Circulating Endothelial Cells in Acute Myocardial Infarction. Science Translational Medicine 4(126): 126ra33-126ra33.
7. Davis, J.; Lantz, E.; Page, D.; Struyf, J.; Peissig, P.; Vidaillet, H.; and Caldwell, M. 2008. Machine Learning for Personalized Medicine: Will This Drug Give Me a Heart Attack. In Proceedings of the 25th International Conference on Machine Learning (ICML). Princeton, NJ: International Machine Learning Society.
8. Detrano, R., and Janosi, A. 1989. International Application of a New Probability Algorithm for the Diagnosis of Coronary Artery Disease. The American Journal of Cardiology 64(5): 304-310.
9. Dietterich, T.; Ashenfelter, A.; and Bulatov, Y. 2004. Training Conditional Random Fields via Gradient Tree Boosting. In Proceedings of the 24th International Conference on Machine Learning (ICML). Princeton, NJ: International Machine Learning Society.
10. Friedman, J. H. 2001. Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics 29(5): 1189-1232.
11. Getoor, L., and Taskar, B. 2007. Introduction to Statistical Relational Learning. Cambridge, MA: The MIT Press.
12. Greenland, P.; Alpert, J. S.; Beller, G. A.; Benjamin, E. J.; Budoff, M. J.; Fayad, Z. A.; Foster, E.; Hlatky, M.; Hodgson, J. M. B.; and Kushner, F. G. 2010. 2010 ACCF/AHA Guideline for Assessment of Cardiovascular Risk in Asymptomatic Adults. Journal of the American College of Cardiology 56(25): e50-e103.
13. Group, D. P. C. 2002. Prediction of Mortality from Coronary Heart Disease Among Diverse Populations: Is There a Common Predictive Function? Heart 88(3): 222-228.
14. Gutmann, B., and Kersting, K. 2006. Tilde-CRF: Conditional Random Fields for Logical Sequences. In Proceedings of the European Conference on Machine Learning (ECML), Lecture Notes in Computer Science 7523. Berlin: Springer.
15. Hall, M.; Frank, E.; Holmes, G.; Pfahringer, B.; Reutemann, P.; and Witten, I. H. 2009. The WEKA Data Mining Software: An Update. Special Interest Group on Knowledge Discovery and Data Mining Explorations Newsletter 11(1): 10-18.
16. Kersting, K., and Driessens, K. 2008. Non-Parametric Policy Gradients: A Unified Treatment of Propositional and Relational Domains. In Proceedings of the 25th International Conference on Machine Learning (ICML). Princeton, NJ: International Machine Learning Society.
17. Manson, J. A. E.; Tosteson, H.; Ridker, P. M.; Satterfield, S.; Hebert, P.; O'Connor, G. T.; Buring, J. E.; and Hennekens, C. H. 1992. The Primary Prevention of Myocardial Infarction. New England Journal of Medicine 326(21): 1406-1416.
18. McCarty, C. A.; Wilke, R. A.; Giampietro, P. F.; Wesbrook, S. D.; and Caldwell, M. D. 2005. Marshfield Clinic Personalized Medicine Research Project (PMRP: Design, Methods and Recruitment for a Large Population-Based Biobank. Personalized Medicine 2(1): 49-79.
19. McCarty, C. A.; Peissig, P.; Caldwell, M. D.; and Wilke, R. A. 2008. The Marshfield Clinic Personalized Medicine Research Project: 2008 Scientific Update and Lessons Learned in the First 6 Years. Personalized Medicine 5(5): 529-542.
20. Natarajan, S.; Joshi, S.; Tadepalli, P.; Kristian, K.; and Shavlik, J. 2011a. Imitation Learning in Relational Domains: A Functional-Gradient Boosting Approach. In Proceedings of the 20th Interational Joint Conferences on Artificial Intelligence (IJCAI). Menlo Park, CA: AAAI Press.
21. Natarajan, S.; Khot, T.; Kersting, K.; Guttmann, B.; and Shavlik, J. 2011b. Gradient-Based Boosting for Statistical Relational Learning: The Relational Dependency Network Case. Machine Learning 86(1): 25-56.
22. Natarajan, S.; Kersting, K.; Joshi, S.; Saldana, S.; Ip, E.; Jacobs, D.; and Carr, J. 2012. Early Prediction of Coronary Artery Calcification Levels Using Statistical Relational Learning. Paper presented at the Workshop on Machine Learning for Clinical Data Analysis. Edinburgh, Scotland, 30 June-1 July.
23. Neville, J.; Jensen, D.; Friedland, L.; and Hay, M. 2003. Learning Relational Probability Trees. In Proceedings of the 9th Knowledge Discovery and Data Mining (KDD) Conference. New York: Association for Computing Machinery.
24. Resnic, F. S.; Popma, J. J.; and Ohno-Machado, L. 2000. Development and Evaluation of Models to Predict Death and Myocardial Infarction Following Coronary Angioplasty and Stenting. In Proceedings of the American Medical Informatics Association Symposium, 690. Washington, DC: National Institutes of Health.
25. Roger, V. L.; Go, A. S.; Lloyd-Jones, D. M.; Adams, R. J.; Berry, J. D.; Brown, T. M.; Carnethon, M. R.; Dai, S.; de Simone, G.; and Ford, E. S. 2011. Heart Disease and Stroke Statistics 2011 Update. Circulation 123(4): e18-e209.
26. Sutton, R.; McAllester, D.; Singh, S.; and Mansour, Y. 2000. Policy Gradient Methods for Reinforcement Learning with Function Approximation. In Proceedings of the 14th Neural Information Processing Systems (NIPS) Conference. Cambridge, MA: The MIT Press.
27. Syed, Z.; Stultz, C. M.; Scirica, B. M.; and Guttag, J. V. 2011. Computationally Generated Cardiac Biomarkers for Risk Stratification After Acute Coronary Syndrome. Science Translational Medicine 3(102): 102ra95-102ra95.
28. Wilson, P. W. F.; D'Agostino, R. B.; Levy, D.; Belanger, A. M.; Silbershatz, H.; and Kannel, W. B. 1998. Prediction of Coronary Heart Disease Using Risk Factor Categories. Circulation 97(18): 1837.