A probabilistic data-driven framework for scoring the preoperative recipient-donor heart transplant survival

Decision Support Systems

Volumn 86, Issue , 2016, Pages 1-12

  Dag, Ali ^a   Topuz, Kazim ^b   Oztekin, Asil ^c   Bulur, Serkan ^d   Megahed, Fadel M ^a  

^a AUBURN UNIVERSITY   (United States)

^b Wichita State University   (United States)

^c UNIVERSITY OF MASSACHUSETTS LOWELL   (United States)

^d ISTANBUL MEDENIYET UNIVERSITY   (Turkey)

Author keywords

Bayesian Belief Networks; Data mining; Genetic algorithms; Healthcare analytics; Medical decision making; United Network for Organ Sharing (UNOS)

Indexed keywords

DATA MINING; DECISION SUPPORT SYSTEMS; GENETIC ALGORITHMS; NEURAL NETWORKS; TREES (MATHEMATICS);

CLASSIFICATION AND REGRESSION TREE; CONDITIONAL PROBABILITIES; DATA MINING MODELS; DATA SCIENCE; DECISION SUPPORT TOOLS; EXPLANATORY VARIABLES; PREDICTIVE PERFORMANCE; UNITED NETWORK FOR ORGAN SHARING (UNOS);

BAYESIAN NETWORKS;

EID: 84980053542     PISSN: 01679236     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dss.2016.02.007     Document Type: Article

References (87)

1. What is heart failure? 2012 [cited 2014 01/26]; Available from: http://www.nhlbi.nih.gov/health/health-topics/topics/hf/
2. J. López-Sendón The heart failure epidemic Medicographia 33 4 2011 363 369
3. What is heart transplant? 2012 01/03/2012 01/31/2014]; Available from: http://www.nhlbi.nih.gov/health/health-topics/topics/ht/
4. What to expect before a heart transplant? 2012 [cited 2014 01/26]; Available from: http://www.nhlbi.nih.gov/health/health-topics/topics/ht/before.html
5. M. Cruz-RamI'rez, C. Hervás-MartI'nez, J.C. Fernández, J. Briceño, and M. de la Mata Multi-objective evolutionary algorithm for donor-recipient decision system in liver transplants European Journal of Operational Research 222 2 2012 317 327
6. Heart transplant 2015 01/26/2015]; Available from: http://my.clevelandclinic.org/services/hic-Surgical-Treatments-for-Heart-Failure/transplant
7. A. Levy A decision-rule for transplanting non-cadaveric organs European Journal of Operational Research 164 2 2005 548 554
8. R. Greco, T. Papalia, D. Lofaro, S. Maestripieri, D. Mancuso, and R. Bonofiglio Decisional trees in renal transplant follow-up Transplantation Proceedings 42 4 2010 1134 1136
9. A. Kusiak, B. Dixon, and S. Shah Predicting survival time for kidney dialysis patients: a data mining approach Computers in Biology and Medicine 35 4 2005 311 327
10. R.S. Lin, S.D. Horn, J.F. Hurdle, and A.S. Goldfarb-Rumyantzev Single and multiple time-point prediction models in kidney transplant outcomes Journal of Biomedical Informatics 41 6 2008 944 952
11. B. Taati, J. Snoek, D. Aleman, and A. Ghavamzadeh Data mining in bone marrow transplant records to identify patients with high odds of survival IEEE Journal of Biomedical Health Informatics 18 1 2014 21 27
12. A. Dag, F.M. Megahed, A. Oztekin, and A. Yucel A data analytics approach to predict heart transplant success Decision Support Systems 2016 (under review)
13. D.F. Del Rizzo, A.H. Menkis, P.W. Pflugfelder, R.J. Novick, F.N. McKenzie, W.D. Boyd, and W.J. Kostuk The role of donor age and ischemic time on survival following orthotopic heart transplantation Journal of Heart and Lung Transplantation 18 4 1999 310 319
14. S.G. Drakos, A.G. Kfoury, E.M. Gilbert, J.W. Long, J.C. Stringham, E.H. Hammond, K.W. Jones, D.A. Bull, M.E. Hagan, J.W. Folsom, B.D. Horne, and D.G. Renlund Multivariate predictors of heart transplantation outcomes in the era of chronic mechanical circulatory support The Annals of Thoracic Surgery 83 1 2007 62 67
15. D. Gupta, V. Piacentino III, M. Macha, A.K. Singhal, J.P. Gaughan, J.B. McClurken, B.I. Goldman, C.A. Fisher, D. Beltramo, J. Monacchio, H.J. Eisen, and S. Furukawa Effect of older donor age on risk for mortality after heart transplantation The Annals of Thoracic Surgery 78 3 2004 890 899
16. A. Al-Khaldi, P.E. Oyer, and R.C. Robbins Outcome analysis of donor gender in heart transplantation Journal of Heart and Lung Transplantation 25 4 2006 461 468
17. Y.S. Tjang, G.J. van der Heijden, G. Tenderich, D.E. Grobbee, and R. Korfer Survival analysis in heart transplantation: results from an analysis of 1290 cases in a single center European Journal of Cardiothorac Surgery 33 5 2008 856 861
18. A. Kilic, E.S. Weiss, T.J. George, G.J. Arnaoutakis, D.D. Yuh, A.S. Shah, and J.V. Conte What predicts long-term survival after heart transplantation? An analysis of 9400 ten-year survivors The Annals of Thoracic Surgery 93 3 2012 699 704
19. K.N. Hong, A. Iribarne, B. Worku, H. Takayama, A.C. Gelijns, Y. Naka, V. Jeevanandam, and M.J. Russo Who is the high-risk recipient? Predicting mortality after heart transplant using pretransplant donor and recipient risk factors The Annals of Thoracic Surgery 92 2 2011 520 527 (discussion 527)
20. A.O. Zuckermann, P. Ofner, C. Holzinger, M. Grimm, R. Mallinger, G. Laufer, and E. Wolner Pre- and early postoperative risk factors for death after cardiac transplantation: a single center analysis Transplant International 13 1 2000 28 34
21. A. Oztekin, D. Delen, and Z. Kong Predicting the graft survival for heart-lung transplantation patients: an integrated data mining methodology International Journal of Medical Informatics 78 12 2009 e84 e96
22. B. Kaplan, and J. Schold Transplantation: neural networks for predicting graft survival Nature Reviews Nephrology 5 4 2009 190 192
23. N. Nakayama, M. Oketani, Y. Kawamura, M. Inao, S. Nagoshi, K. Fujiwara, H. Tsubouchi, and S. Mochida Algorithm to determine the outcome of patients with acute liver failure: a data-mining analysis using decision trees Journal of Gastroenterology 47 6 2012 664 677
24. T.S. Brown, E.A. Elster, K. Stevens, J.C. Graybill, S. Gillern, S. Phinney, M.O. Salifu, and R.M. Jindal Bayesian modeling of pretransplant variables accurately predicts kidney graft survival American Journal of Nephrology 36 6 2012 561 569
25. N. Hoot, and D. Aronsky Using Bayesian networks to predict survival of liver transplant patients AMIA Annual Symposium Proceedings 2005 American Medical Informatics Association
26. D. Heckerman A tutorial on learning with Bayesian networks Innovations in Bayesian Networks 2008 Springer 33 82
27. UNOS About us 2014 2014/6/2]; Available from: http://www.unos.org/contact/index.php
28. R. Kohavi, and G.H. John Wrappers for feature subset selection Artificial Intelligence 97 1 1997 273 324
29. P. Leray, and P. Gallinari Feature selection with neural networks Behaviormetrika 26 1999 145 166
30. R. Setiono, and H. Liu Neural-network feature selector IEEE Transactions on Neural Networks 8 3 1997 654 662
31. V. Sugumaran, V. Muralidharan, and K.I. Ramachandran Feature selection using decision tree and classification through proximal support vector machine for fault diagnostics of roller bearing Mechanical Systems and Signal Processing 21 2 2007 930 942
32. D. Sheppard, D. McPhee, C. Darke, B. Shrethra, R. Moore, A. Jurewitz, and A. Gray Predicting cytomegalovirus disease after renal transplantation: an artificial neural network approach International Journal of Medical Informatics 54 1 1999 55 76
33. J. Han, and M. Kamber Data Mining: Concepts and Techniques third ed. 2011 Elsevier Burlington, MA 703
34. T. Hastie, R. Tibshirani, J. Friedman, T. Hastie, J. Friedman, and R. Tibshirani The Elements of Statistical Learning: Data Mining, Inference, and Prediction vol. 2 2009 Springer New York, USA 745
35. R. Kohavi A study of cross-validation and bootstrap for accuracy estimation and model selection Ijcai 1995
36. D.L. Olson, and D. Delen Advanced Data Mining Techniques 2008 Springer Publishing Company, Incorporated
37. L.B.J.H.F.R.A. Olshen, and C.J. Stone Classification and Regression Trees 1984 Wadsworth International Group
38. J.R. Quinlan Induction of decision trees Machine Learning 1 1 1986 81 106
39. J.R. Quinlan C4. 5: Programs for Machine Learning vol. 1 1993 Morgan Kaufmann
40. M. Sordo Introduction to neural networks in healthcare Open Clinical: Knowledge Management for Medical Care, Harvard University 2002 (Online)
41. V.G.J.S.R. Mollineda, and R.A.J. Sotoca The Class Imbalance Problem in Pattern Classification and Learning 2007
42. M. Molaie, R. Falahian, S. Gharibzadeh, S. Jafari, and J.C. Sprott Artificial neural networks: powerful tools for modeling chaotic behavior in the nervous system Frontiers in Computer Neuroscience 8 2014 40
43. A. Saltelli Making best use of model evaluations to compute sensitivity indices Computer Physics Communications 145 2 2002 280 297
44. A. Saltelli, S. Tarantola, F. Campolongo, and M. Ratto Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models 2004 John Wiley & Sons
45. R.T. Clemen Combining forecasts: a review and annotated bibliography International Journal of Forecasting 5 4 1989 559 583
46. A. Graefe, J.S. Armstrong, R.J. Jones, and A.G. Cuzan Combining forecasts: an application to elections International Journal of Forecasting 30 1 2014 43 54
47. D. Delen, R. Sharda, and P. Kumar Movie forecast guru: A web-based DSS for Hollywood managers Decision Support Systems 43 4 2007 1151 1170
48. S. Vinterbo, and L. Ohno-Machado A genetic algorithm to select variables in logistic regression: example in the domain of myocardial infarction Proceedings of the AMIA Symposium 1999 984 988
49. B. Sahiner, H.P. Chan, D. Wei, N. Petrick, M.A. Helvie, D.D. Adler, and M.M. Goodsitt Image feature selection by a genetic algorithm: application to classification of mass and normal breast tissue Medical Physics 23 10 1996 1671 1684
50. N. Lavrač Selected techniques for data mining in medicine Artificial Intelligence in Medicine 16 1 1999 3 23
51. A.E. Hoerl, and R.W. Kennard Ridge regression: biased estimation for nonorthogonal problems Technometrics 42 1 2000 80 86
52. D.R. Cox, and D. Oakes Analysis of Survival Data vol. 21 1984 CRC Press
53. L. Ohno-Machado Modeling medical prognosis: survival analysis techniques Journal of Biomedical Informatics 34 6 2001 428 439
54. C. Bardage, and D.G. Isacson Hypertension and health-related quality of life. An epidemiological study in Sweden Journal of Clinical Epidemiology 54 2 2001 172 181
55. R.C. Bourge, D.C. Naftel, M.R. Costanzo-Nordin, J.K. Kirklin, J.B. Young, S.H. Kubo, M.T. Olivari, and E.K. Kasper Pretransplantation risk factors for death after heart transplantation: a multiinstitutional study. The Transplant Cardiologists Research Database Group Journal of Heart and Lung Transplantation 12 4 1993 549 562
56. J. Stehlik, L.B. Edwards, A.Y. Kucheryavaya, C. Benden, J.D. Christie, F. Dobbels, R. Kirk, A.O. Rahmel, and M.I. Hertz The registry of the international society for heart and lung transplantation: twenty-eighth adult heart transplant report-2011 Journal of Heart and Lung Transplantation 30 10 2011 1078 1094
57. H.M. Kauffman, M.A. McBride, and F.L. Delmonico First report of the united network for organ sharing transplant tumor registry: donors with a history of cancer Transplantation 70 12 2000 1747 1751
58. J. Pearl Bayesian networks: A model of self-activated memory for evidential reasoning http://ftp.cs.ucla.edu/tech-report/198-reports/850017.pdf 1985
59. J.R. Anderson, R.S. Michalski, J.G. Carbonell, and T.M. Mitchell Machine Learning: An Artificial Intelligence Approach vol. 2 1986 Morgan Kaufmann
60. D. Aronsky, and P.J. Haug Automatic identification of patients eligible for a pneumonia guideline Proceedings of the AMIA Symposium 2000 American Medical Informatics Association
61. C.C. Bunn, M. Du, K. Niu, T.R. Johnson, W.S.C. Poston, and J.P. Foreyt Predicting the risk of obesity using a Bayesian network Proceedings of the AMIA Symposium 1999 American Medical Informatics Association
62. E. Burnside, D. Rubin, and R. Shachter A Bayesian network for mammography Proceedings of the AMIA Symposium 2000 American Medical Informatics Association
63. E.S. Burnside, D.L. Rubin, J.P. Fine, R.D. Shachter, G.A. Sisney, and W.K. Leung Bayesian network to predict breast cancer risk of mammographic microcalcifications and reduce number of benign biopsy results: initial experience Radiology 240 3 2006 666 673
64. P.W. Hamilton, R. Montironi, W. Abmayr, M. Bibbo, N. Anderson, D. Thompson, and P.H. Bartels Clinical applications of Bayesian belief networks in pathology Pathologica 87 3 1995 237 245
65. R. Montironi, P.H. Bartels, D. Thompson, M. Scarpelli, and P.W. Hamilton Prostatic intraepithelial neoplasia (PIN). Performance of Bayesian belief network for diagnosis and grading The Journal of Pathology 177 2 1995 153 162
66. G.C. Sakellaropoulos, and G.C. Nikiforidis Development of a Bayesian network for the prognosis of head injuries using graphical model selection techniques Methods of Information in Medicine 38 1999 37 42
67. N. Friedman Inferring cellular networks using probabilistic graphical models Science 303 5659 2004 799 805
68. X. Jiang, D. Xue, A. Brufsky, S. Khan, and R. Neapolitan A new method for predicting patient survivorship using efficient Bayesian network learning Cancer Informatics 13 2014 47 57
69. S.-M. Lee, and P.A. Abbott Bayesian networks for knowledge discovery in large datasets: basics for nurse researchers Journal of Biomedical Informatics 36 4 2003 389 399
70. D. Koller, and N. Friedman Probabilistic Graphical Models: Principles and Techniques 2009 MIT Press
71. N. Friedman, D. Geiger, and M. Goldszmidt Bayesian network classifiers Machine Learning 29 2-3 1997 131 163
72. C.K. Chow, and C.N. Liu Approximating discrete probability distributions with dependence trees IEEE Transactions on Information Theory 14 3 1968 462 - +
73. S. Andreassen, C. Riekehr, B. Kristensen, H.C. Schønheyder, and L. Leibovici Using probabilistic and decision - theoretic methods in treatment and prognosis modeling Artificial Intelligence in Medicine 15 2 1999 121 134
74. D.E. Heckerman, E.J. Horvitz, and B.N. Nathwani Toward normative expert systems.1. The Pathfinder Project Methods of Information in Medicine 31 2 1992 90 105
75. P. Lucas, H. Boot, and B. Taal Computer-based decision support in the management of primary gastric non-Hodgkin lymphoma Change 1 1998 4
76. P.J.F. Lucas, N.C. de Bruijn, K. Schurink, and A. Hoepelman A probabilistic and decision-theoretic approach to the management of infectious disease at the ICU Artificial Intelligence in Medicine 19 3 2000 251 279
77. L.C. van der Gaag, S. Renooij, C.L.M. Witteman, B.M.P. Aleman, and B.G. Taal Probabilities for a probabilistic network: a case study in oesophageal cancer Artificial Intelligence in Medicine 25 2 2002 123 148
78. P.J. Lucas, L.C. van der Gaag, and A. Abu-Hanna Bayesian networks in biomedicine and health-care Artificial Intelligence in Medicine 30 3 2004 201 214
79. B. Yet, K. Bastani, H. Raharjo, S. Lifvergren, W. Marsh, and B. Bergman Decision support system for warfarin therapy management using Bayesian networks Decision Support Systems 55 2 2013 488 498
80. M.R. Mehra, J. Kobashigawa, R. Starling, S. Russell, P.A. Uber, J. Parameshwar, P. Mohacsi, S. Augustine, K. Aaronson, and M. Barr Listing criteria for heart transplantation: International Society for Heart and Lung Transplantation guidelines for the care of cardiac transplant candidates - 2006 The Journal of Heart and Lung Transplantation 25 9 2006 1024 1042
81. J. Davidson, A. Wilkinson, J. Dantal, F. Dotta, H. Haller, D. Hernandez, B.L. Kasiske, B. Kiberd, A. Krentz, C. Legendre, P. Marchetti, M. Markell, F.J. van der Woude, and D.C. Wheeler New-onset diabetes after transplantation: 2003 international consensus guidelines Transplantation 75 10 2003 SS3 SS24
82. J.G. Allen, E.S. Weiss, G.J. Arnaoutakis, S.D. Russell, W.A. Baumgartner, A.S. Shah, and J.V. Conte Insurance and education predict long-term survival after orthotopic heart transplantation in the United States The Journal of Heart and Lung Transplantation 31 1 2012 52 60
83. S. Hussain, P. Lenner, J. Sundquist, and K. Hemminki Influence of education level on cancer survival in Sweden Annals of Oncology 19 1 2008 156 162
84. Y. Gerber, S.A. Weston, J.M. Killian, T.M. Therneau, S.J. Jacobsen, and V.L. Roger Neighborhood income and individual education: effect on survival after myocardial infarction Mayo Clinic Proceedings 2008 Elsevier
85. S. Rosso, F. Faggiano, R. Zanetti, and G. Costa Social class and cancer survival in Turin, Italy Journal of Epidemiology and Community Health 51 1 1997 30 34
86. S. Geller, and T. Connolly The influence of psychosocial factors on heart transplantation decisions and outcomes Journal of Transplant Coordination 7 4 1997 173 179
87. P. Shapiro, D. Williams, A. Foray, I. Gelman, N. Wukich, and R. Sciacca Psychosocial evaluation and prediction of compliance problems and morbidity after heart transplantation Transplantation 60 12 1995 1462