Looking beyond historical patient outcomes to improve clinical models

Science Translational Medicine

Volumn 4, Issue 131, 2012, Pages

  Chia, Chih Chun ^a   Rubinfeld, Ilan ^b   Scirica, Benjamin M ^c,d   McMillan, Sean ^a   Gurm, Hitinder S ^a   Syed, Zeeshan ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

^b HENRY FORD HEALTH SYSTEM   (United States)

^c HARVARD MEDICAL SCHOOL   (United States)

^d BRIGHAM AND WOMEN S HOSPITAL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ACUTE CORONARY SYNDROME; AREA UNDER THE CURVE; ARTICLE; BLEEDING; CALIBRATION; CARDIOVASCULAR DISEASE; CEREBROVASCULAR ACCIDENT; CLASSIFICATION ALGORITHM; CLINICAL DECISION MAKING; COHORT ANALYSIS; COMA; CONTROLLED STUDY; COST EFFECTIVENESS ANALYSIS; GRAFT FAILURE; HEART ARREST; HEART INFARCTION; HUMAN; INTEGRATED DISCRIMINATION IMPROVEMENT; INTERMETHOD COMPARISON; KIDNEY FAILURE; LEARNING CURVE; LOGISTIC REGRESSION ANALYSIS; LUNG EMBOLISM; MORBIDITY; NONBIOLOGICAL MODEL; OUTCOME ASSESSMENT; PARAMETERS; PERCUTANEOUS CORONARY INTERVENTION; PERIPHERAL NERVE INJURY; PREDICTIVE VALUE; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; RELIABILITY; RISK ASSESSMENT; SUPPORT VECTOR MACHINE; VALIDATION PROCESS; ALGORITHM; ARTIFICIAL INTELLIGENCE; COMPARATIVE STUDY; DATA MINING; DECISION SUPPORT SYSTEM; DISCRIMINANT ANALYSIS; HEALTH CARE QUALITY; MORTALITY; POSTOPERATIVE COMPLICATION; PREVALENCE; REGISTER; REPRODUCIBILITY; RISK FACTOR; STANDARD; STATISTICAL MODEL; STATISTICS; SURGERY; TOTAL QUALITY MANAGEMENT; TRANSLUMINAL CORONARY ANGIOPLASTY; TREATMENT OUTCOME; UNITED STATES;

ACUTE CORONARY SYNDROME; ALGORITHMS; ANGIOPLASTY, BALLOON, CORONARY; ARTIFICIAL INTELLIGENCE; DATA MINING; DECISION SUPPORT TECHNIQUES; DISCRIMINANT ANALYSIS; HUMANS; LOGISTIC MODELS; MODELS, STATISTICAL; MYOCARDIAL INFARCTION; POSTOPERATIVE COMPLICATIONS; PREVALENCE; QUALITY IMPROVEMENT; QUALITY INDICATORS, HEALTH CARE; REGISTRIES; REPRODUCIBILITY OF RESULTS; RISK ASSESSMENT; RISK FACTORS; SURGICAL PROCEDURES, OPERATIVE; TREATMENT OUTCOME; UNITED STATES;

EID: 84860366215     PISSN: 19466234     EISSN: 19466242     Source Type: Journal    
DOI: 10.1126/scitranslmed.3003561     Document Type: Article

References (21)

1. L. K. Newby, M. V. Bhapkar, H. D. White, E. J. Topol, F. C. Dougherty, R. A. Harrington, M. C. Smith, L. F. Asarch, R. M. Califf; SYMPHONY and 2nd SYMPHONY Investigators, Predictors of 90-day outcome in patients stabilized after acute coronary syndromes. Eur. Heart J. 24, 172-181 (2003).
2. C. P. Cannon, S. Husted, R. A. Harrington, B. M. Scirica, H. Emanuelsson, G. Peters, R. F. Storey; DISPERSE-2 Investigators, Safety, tolerability, and initial efficacy of AZD6140, the first reversible oral adenosine diphosphate receptor antagonist, compared with clopidogrel, in patients with non-ST-segment elevation acute coronary syndrome: Primary results of the DISPERSE-2 trial. J. Am. Coll. Cardiol. 50, 1844-1851 (2007).
3. S. F. Khuri, The NSQIP: A new frontier in surgery. Surgery 138, 837-843 (2005).
4. M. Moscucci, D. Share, E. Kline-Rogers, M. O'Donnell, A. Maxwell-Eward, W. L. Meengs, V. L. Clark, P. Kraft, A. C. De Franco, J. L. Chambers, K. Patel, J. G. McGinnity, K. A. Eagle; Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2), The Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) collaborative quality improvement initiative in percutaneous coronary interventions. J. Interv. Cardiol. 15, 381-386 (2002).
5. V. Vapnik, Statistical Learning Theory (Wiley, New York, 1998).
6. P. L. Schilling, J. B. Dimick, J. D. Birkmeyer, Prioritizing quality improvement in general surgery. J. Am. Coll. Surg. 207, 698-704 (2008).
7. N. V. Chawla, N. Japkowicz, A. Kotcz, Editorial: Special issue on learning from imbalanced data sets. ACM SIGKDD Explor. Newsl. 6, 1-6 (2004).
8. B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, R. C. Williamson, Estimating the support of a high-dimensional distribution. Neural Comput. 13, 1443-1471 (2001).
9. Z Syed, I. Rubinfeld, Unsupervised risk stratification in clinical datasets: Identifying patients at risk of rare outcomes, in Proceedings of the 27th International Conference on Machine Learning (ICML-10), Haifa, Israel, 21 to 24 June 2010.
10. O. Chapelle, B. Scholkopf, A. Zien, Semi-Supervised Learning (MIT Press, Cambridge, MA, 2010).
11. W. S. Noble, What is a support vector machine? Nat. Biotechnol. 24, 1565-1567 (2006).
12. T. Verplancke, S. Van Looy, D. Benoit, S. Vansteelandt, P. Depuydt, F. De Turck, J. Decruyenaere, Support vector machine versus logistic regression modeling for prediction of hospital mortality in critically ill patients with haematological malignancies. BMC Med. Inform. Decis. Mak. 8, 56 (2008).
13. J. Platt, Probabilistic outputs for support vector machines, in Advances in Large Margin Classifiers, A. J. Smola, P. L. Bartlett, B. Schölkopf, D. Schuurmans, Eds. (MIT Press, Cambridge, MA, 2000).
14. H. T. Lin, C. J. Lin, R. C. Weng, A note on Platt's probabilistic outputs for support vector machines. Mach. Learn. 68, 267-276 (2007).
15. K. Morik, P. Brockhausen, T. Joachims, Combining statistical learning with a knowledge-based approach - A case study in intensive care monitoring, in Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, 27 to 30 June 1999 (Morgan Kaufman Publishers, San Francisco, CA, 1999).
16. N. V. Chawla, K. W. Bowyer, L. O. Hall, W. P. Kegelmeyer, SMOTE: Synthetic minority oversampling technique. J. Artif. Int. Res. 16, 321-357 (2002).
17. D. A. Morrow, B. M. Scirica, E. Karwatowska-Prokopczuk, S. A. Murphy, A. Budaj, S. Varshavsky, A. A. Wolff, A. Skene, C. H. McCabe, E. Braunwald; MERLIN-TIMI 36 Trial Investigators, Effects of ranolazine on recurrent cardiovascular events in patients with non-ST-elevation acute coronary syndromes: The MERLIN-TIMI 36 randomized trial. JAMA 297, 1775-1783 (2007).
18. E. R. DeLong, D. M. DeLong, D. L. Clarke-Pearson, Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics 44, 837-845 (1988).
19. M. J. Pencina, R. B. D'Agostino Sr., R. B. D'Agostino Jr., R. S. Vasan, Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond. Stat. Med. 27, 157-172 (2008).
20. D. W. Hosmer, S. Lemeshow, Applied Logistic Regression (Wiley, New York, 2000).
21. C. Perlich, F. Provost, J. S. Simonoff, Tree induction vs. logistic regression: A learning-curve analysis. J. Mach. Learn. Res. 4, 211-255 (2003).