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Adaptively Weighted Top-N Recommendation for Organ Matching

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Published:15 October 2021Publication History
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Abstract

Reducing the shortage of organ donations to meet the demands of patients on the waiting list has being a major challenge in organ transplantation. Because of the shortage, organ matching decision is the most critical decision to assign the limited viable organs to the most “suitable” patients. Currently, organ matching decisions are only made by matching scores calculated via scoring models, which are built by the first principles. However, these models may disagree with the actual post-transplantation matching performance (e.g., patient's post-transplant quality of life (QoL) or graft failure measurements). In this paper, we formulate the organ matching decision-making as a top-N recommendation problem and propose an Adaptively Weighted Top-N Recommendation (AWTR) method. AWTR improves performance of the current scoring models by using limited actual matching performance in historical datasets as well as the collected covariates from organ donors and patients. AWTR sacrifices the overall recommendation accuracy by emphasizing the recommendation and ranking accuracy for top-N matched patients. The proposed method is validated in a simulation study, where KAS [60] is used to simulate the organ-patient recommendation response. The results show that our proposed method outperforms seven state-of-the-art top-N recommendation benchmark methods.

REFERENCES

  1. [1] Adomavicius G. and Tuzhilin A.. 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17, 6 (June 2005), 734749. DOI: https://doi.org/10.1109/TKDE.2005.99 Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. [2] Ahmadvand Sahar and Pishvaee Mir Saman. 2018. An efficient method for kidney allocation problem: A credibility-based fuzzy common weights data envelopment analysis approach. Health Care Management Science 21, 4 (December 2018), 587603. DOI: https://doi.org/10.1007/s10729-017-9414-6Google ScholarGoogle Scholar
  3. [3] Ashlagi I., Gilchrist D. S., Roth A. E., and Rees M. A.. 2011. Nonsimultaneous chains and dominos in kidney-paired donation—Revisited. American Journal of Transplantation 11, 5 (2011), 984994. DOI: https://doi.org/10.1111/j.1600-6143.2011.03481.xGoogle ScholarGoogle ScholarCross RefCross Ref
  4. [4] Bertsimas Dimitris, Farias Vivek F., and Trichakis Nikolaos. 2013. Fairness, efficiency, and flexibility in organ allocation for kidney transplantation. Operations Research 61, 1 (February 2013), 7387. DOI: https://doi.org/10.1287/opre.1120.1138 Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. [5] Boyd Stephen, Parikh Neal, Chu Eric, Peleato Borja, and Eckstein Jonathan. 2011. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends® in Machine Learning 3, 1 (January 2011), 1122. DOI: https://doi.org/10.1561/2200000016 Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. [6] Cai Jian-Feng, Candès Emmanuel J., and Shen Zuowei. 2010. A singular value thresholding algorithm for matrix completion. SIAM Journal on Optimization 20, 4 (January 2010), 19561982. DOI: https://doi.org/10.1137/080738970 Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. [7] Candès Emmanuel and Recht Benjamin. 2012. Exact matrix completion via convex optimization. Communications of the ACM 55, 6 (June 2012), 111119. DOI: https://doi.org/10.1145/2184319.2184343 Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. [8] Cao Zhe, Qin Tao, Liu Tie-Yan, Tsai Ming-Feng, and Li Hang. 2007. Learning to rank: From pairwise approach to listwise approach. In Proceedings of the 24th International Conference on Machine learning (ICML’07), Association for Computing Machinery, Corvalis, Oregon, USA, 129136. DOI: https://doi.org/10.1145/1273496.1273513 Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. [9] Chen Xioayu, Lau Nathan, and Jin Ran. 2021. PRIME: A personalized recommender system for information visualization methods via extended matrix completion. ACM Trans. Interact. Intell. Syst. 11, 1, Article 7 (April 2021), 30 pages.Google ScholarGoogle Scholar
  10. [10] Cheng Yao, Yin Liang, and Yu Yong. 2014. Low rank sparse linear methods for top-n recommendations. In 2014 IEEE International Conference on Data Mining, 9099. DOI: https://doi.org/10.1109/ICDM.2014.112 Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. [11] Cremonesi Paolo, Koren Yehuda, and Turrin Roberto. 2010. Performance of recommender algorithms on top-n recommendation tasks. In Proceedings of the Fourth ACM Conference on Recommender Systems (RecSys’10), Association for Computing Machinery, Barcelona, Spain, 3946. DOI: https://doi.org/10.1145/1864708.1864721 Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. [12] Croft Bruce, Metzler Donald, and Strohman Trevor. 2009. Search Engines: Information Retrieval in Practice (1st ed.). Addison-Wesley Publishing Company, USA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. [13] David Israel and Yechiali Uri. 1995. One-attribute sequential assignment match processes in discrete time. Operations Research 43, 5 (October 1995), 879884. DOI: https://doi.org/10.1287/opre.43.5.879 Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. [14] Deshpande Mukund and Karypis George. 2004. Item-based top-N recommendation algorithms. ACM Transactions on Information Systems 22, 1 (January 2004), 143177. DOI: https://doi.org/10.1145/963770.963776 Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. [15] Dongping G., Yang L., Xiaobei S., Junxiang X., Yuan Y., and Hui C.. 2013. Long-term factors influencing survival after kidney transplantation. Transplantation Proceedings 45, 1 (January 2013), 129133. DOI: https://doi.org/10.1016/j.transproceed.2012.08.014Google ScholarGoogle Scholar
  16. [16] Donoho David L. and Johnstone Iain M.. 1994. Ideal spatial adaptation by wavelet shrinkage. Biometrika 81, 3 (September 1994), 425455. DOI: https://doi.org/10.1093/biomet/81.3.425Google ScholarGoogle ScholarCross RefCross Ref
  17. [17] Gunawardana Asela and Shani Guy. 2015. Evaluating recommender systems. In Recommender Systems Handbook, Ricci Francesco, Rokach Lior and Shapira Bracha (eds.). Springer US, Boston, MA, 265308. DOI: https://doi.org/10.1007/978-1-4899-7637-6_8Google ScholarGoogle ScholarCross RefCross Ref
  18. [18] Gundogar Emin, Duran Fatih M., Canbolat Yavuz B., and Turkmen Aydn. 2005. Fuzzy organ allocation system for cadaveric kidney transplantation. Transplantation 80, 12 (December 2005), 16481653. DOI: https://doi.org/10.1097/01.tp.0000183287.04630.05Google ScholarGoogle Scholar
  19. [19] Guo Huifeng, Tang Ruiming, Ye Yunming, Li Zhenguo, and He Xiuqiang. 2017. DeepFM: A factorization-machine based neural network for CTR prediction. (March 2017). Retrieved March 26, 2021 from http://arxiv.org/abs/1703.04247v1Google ScholarGoogle Scholar
  20. [20] Guo Ke, Han Deren, Wang David Z. W., and Wu Tingting. 2017. Convergence of ADMM for multi-block nonconvex separable optimization models. Frontiers of Mathematics in China 12, 5 (October 2017), 11391162. DOI: https://doi.org/10.1007/s11464-017-0631-6Google ScholarGoogle Scholar
  21. [21] Hastie Trevor, Tibshirani Robert, and Wainwright Martin. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. CRC Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. [22] He Xiangnan, Liao Lizi, Zhang Hanwang, Nie Liqiang, Hu Xia, and Chua Tat-Seng. 2017. Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web (WWW’17), International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 173182. DOI: https://doi.org/10.1145/3038912.3052569Google ScholarGoogle Scholar
  23. [23] Herlocker Jonathan L., Konstan Joseph A., Borchers Al, and Riedl John. 1999. An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’99), Association for Computing Machinery, Berkeley, California, USA, 230237. DOI: https://doi.org/10.1145/312624.312682 Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. [24] Hofmann Thomas. 2004. Latent semantic models for collaborative filtering. ACM Transactions on Information Systems 22, 1 (January 2004), 89115. DOI: https://doi.org/10.1145/963770.963774 Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. [25] Jain Prateek, Netrapalli Praneeth, and Sanghavi Sujay. 2013. Low-rank matrix completion using alternating minimization. In Proceedings of the Forty-fifth Annual ACM Symposium on Theory of Computing (STOC’13), Association for Computing Machinery, Palo Alto, California, USA, 665674. DOI: https://doi.org/10.1145/2488608.2488693 Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. [26] Jin Ran, Deng Xinwei, Chen Xiaoyu, Zhu Liang, and Zhang Jun. 2019. Dynamic quality-process model in consideration of equipment degradation. 51, 3 (July 2019), 217229.Google ScholarGoogle Scholar
  27. [27] Koren Yehuda. 2010. Factor in the neighbors: Scalable and accurate collaborative filtering. ACM Transactions on Knowledge Discovery from Data 4, 1 (January 2010), 1:1–1:24. DOI: https://doi.org/10.1145/1644873.1644874 Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. [28] Koren Yehuda, Bell Robert, and Volinsky Chris. 2009. Matrix factorization techniques for recommender systems. Computer 42, 8 (August 2009), 3037. DOI: https://doi.org/10.1109/MC.2009.263 Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. [29] Nelder J. A. and Wedderburn R. W. M.. 1972. Generalized linear models. Journal of the Royal Statistical Society: Series A (General) 135, 3 (1972), 370384.Google ScholarGoogle ScholarCross RefCross Ref
  30. [30] Ning Xia and Karypis George. 2011. Sparse linear methods for top-n recommender systems. In 2011 IEEE 11th International Conference on Data Mining, 497506. DOI: https://doi.org/10.1109/ICDM.2011.134 Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. [31] Nosotti M., Dell'Amore A., Diso D., Oggionni T., Aliberti S., Balestro E., Bertani A., Boffini M., Lucianetti A., Luzzi L., Paone G., Parigi P., Pellegrini C., Rocca A., Rottoli P., Santambrogio L., Schiavon M., Solidoro P., Vitulo P., and Tarsia P.. 2017. Selection of candidates for lung transplantation: The first Italian consensus statement. Transplantation Proceedings 49, 4 (May 2017), 702706. DOI: https://doi.org/10.1016/j.transproceed.2017.02.026Google ScholarGoogle Scholar
  32. [32] Pinson C. Wright, Feurer Irene D., Payne Jerita L., Wise Paul E., Shockley Shannon, and Speroff Theodore. 2000. Health-related quality of life after solid organ transplantation: A prospective, multiorgan cohort study. Annals of Surgery 232, 4 (October 2000), 597607. Retrieved September 27, 2020 from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1421192/Google ScholarGoogle Scholar
  33. [33] Rendle Steffen, Freudenthaler Christoph, Gantner Zeno, and Schmidt-Thieme Lars. 2012. Bayesian personalized ranking from implicit feedback. arXiv:1205.2618 [cs, stat] (May 2012). Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. [34] RFI. 2008. Kidney Allocation Concepts: Request for Information. OPTN/UNOS Kidney Transplantation Committee (2008).Google ScholarGoogle Scholar
  35. [35] Ricci Francesco, Rokach Lior, and Shapira Bracha. 2011. Introduction to recommender systems handbook. In Recommender Systems Handbook, Francesco Ricci, Lior Rokach, Bracha Shapira and Paul B. Kantor (eds.). Springer US, Boston, MA, 135. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. [36] Righter Rhonda. 1989. A resource allocation problem in a random environment. Operations Research 37, 2 (April 1989), 329338. DOI: https://doi.org/10.1287/opre.37.2.329 Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. [37] Roth Alvin E., Sönmez Tayfun, and Ünver M. Utku. 2004. Kidney exchange. The Quarterly Journal of Economics 119, 2 (May 2004), 457488. DOI: https://doi.org/10.1162/0033553041382157Google ScholarGoogle ScholarCross RefCross Ref
  38. [38] Jean Ruth R., Wyszewianski Leon, and Herline Gary. 1985. Kidney transplantation: A simulation model for examining demand and supply. Management Science 31, 5 (March 1985), 515526. DOI: https://doi.org/10.1287/mnsc.31.5.515 Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. [39] Salakhutdinov Ruslan and Mnih Andriy. 2007. Probabilistic matrix factorization. In Proceedings of the 20th International Conference on Neural Information Processing Systems (NIPS’07), Curran Associates Inc., Vancouver, British Columbia, Canada, 12571264. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. [40] Sarwar Badrul, Karypis George, Konstan Joseph, and Riedl John. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web, Association for Computing Machinery, Hong Kong, China, 285295. DOI: https://doi.org/10.1145/371920.372071 Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. [41] Schulte K., Klasen V., Vollmer C., Borzikowsky C., Kunzendorf U., and Feldkamp T.. 2018. Analysis of the Eurotransplant Kidney Allocation Algorithm: How should we balance utility and equity? Transplant Proc 50, 10 (December 2018), 30103016. DOI: https://doi.org/10.1016/j.transproceed.2018.08.040Google ScholarGoogle Scholar
  42. [42] Segev Dorry L., Gentry Sommer E., Warren Daniel S., Reeb Brigitte, and Montgomery Robert A.. 2005. Kidney paired donation and optimizing the use of live donor organs. JAMA 293, 15 (April 2005), 18831890. DOI: https://doi.org/10.1001/jama.293.15.1883Google ScholarGoogle ScholarCross RefCross Ref
  43. [43] Steck Harald. 2013. Evaluation of recommendations: Rating-prediction and ranking. In Proceedings of the 7th ACM Conference on Recommender Systems (RecSys’13), Association for Computing Machinery, Hong Kong, China, 213220. DOI: https://doi.org/10.1145/2507157.2507160 Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. [44] Stegall Mark D.. 2010. The right kidney for the right recipient: The status of deceased donor kidney allocation reform. Seminars in Dialysis 23, 3 (2010), 248252. DOI: https://doi.org/10.1111/j.1525-139X.2010.00723.xGoogle ScholarGoogle Scholar
  45. [45] Stegall Mark D., Stock Peter G., Andreoni Kenneth, Friedewald John J., and Leichtman Alan B.. 2017. Why do we have the kidney allocation system we have today? A history of the 2014 kidney allocation system. Human Immunology 78, 1 (January 2017), 48. DOI: https://doi.org/10.1016/j.humimm.2016.08.008Google ScholarGoogle Scholar
  46. [46] Su Xiaoyuan and Khoshgoftaar Taghi M.. 2009. A survey of collaborative filtering techniques. Advances in Artificial Intelligence 2009, (January 2009), 4:2. DOI: https://doi.org/10.1155/2009/421425 Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. [47] Tibshirani Robert. 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological) 58, 1 (1996), 267288. DOI: https://doi.org/10.1111/j.2517-6161.1996.tb02080.xGoogle ScholarGoogle ScholarCross RefCross Ref
  48. [48] Tong Allison, Jan Stephen, Wong Germaine, Craig Jonathan C., Irving Michelle, Chadban Steve, Cass Alan, Marren Niamh, and Howard Kirsten. 2012. Patient preferences for the allocation of deceased donor kidneys for transplantation: A mixed methods study. BMC Nephrology 13, 1 (April 2012), 18. DOI: https://doi.org/10.1186/1471-2369-13-18Google ScholarGoogle Scholar
  49. [49] Wang Yining, Wang Liwei, Li Yuanzhi, He Di, Liu Tie-Yan, and Chen Wei. 2013. A theoretical analysis of NDCG type ranking measures. arXiv:1304.6480 [cs, stat] (April 2013).Google ScholarGoogle Scholar
  50. [50] Wang Yu, Yin Wotao, and Zeng Jinshan. 2019. Global convergence of ADMM in nonconvex nonsmooth optimization. Journal of Scientific Computing 78, 1 (January 2019), 2963. DOI: https://doi.org/10.1007/s10915-018-0757-z Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. [51] Wilson Hugh R. and Cowan Jack D.. 1972. Excitatory and inhibitory interactions in localized populations of model neurons. Biophys J. 12, 1 (January 1972), 124.Google ScholarGoogle ScholarCross RefCross Ref
  52. [52] Wolfe R. A., McCullough K. P., and Leichtman A. B.. 2009. Predictability of survival models for waiting list and transplant patients: calculating LYFT. American Journal of Transplantation 9, 7 (2009), 15231527. DOI: https://doi.org/10.1111/j.1600-6143.2009.02708.xGoogle ScholarGoogle Scholar
  53. [53] Wolfe R. A., McCullough K. P., Schaubel D. E., Kalbfleisch J. D., Murray S., Stegall M. D., and Leichtman A. B.. 2008. Calculating life years from transplant (LYFT): Methods for kidney and kidney-pancreas candidates. American Journal of Transplantation 8, 4p2 (2008), 9971011. DOI: https://doi.org/10.1111/j.1600-6143.2008.02177.xGoogle ScholarGoogle Scholar
  54. [54] Wright Stephen J.. 2015. Coordinate descent algorithms. arXiv:1502.04759 [math] (February 2015).Google ScholarGoogle Scholar
  55. [55] Yang Junfeng and Yuan Xiaoming. 2013. Linearized augmented Lagrangian and alternating direction methods for nuclear norm minimization. Math. Comp. 82, 281 (2013), 301329. DOI: https://doi.org/10.1090/S0025-5718-2012-02598-1Google ScholarGoogle ScholarCross RefCross Ref
  56. [56] Ye Gui-Bo and Xie Xiaohui. 2011. Split Bregman method for large scale fused Lasso. Computational Statistics & Data Analysis 55, 4 (April 2011), 15521569. DOI: https://doi.org/10.1016/j.csda.2010.10.021 Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. [57] Zenios Stefanos A. 2002. Optimal control of a paired-kidney exchange program. Management Science 48, 3 (March 2002), 328342. DOI: https://doi.org/10.1287/mnsc.48.3.328.7732 Google ScholarGoogle ScholarDigital LibraryDigital Library
  58. [58] 2004. WHO | Transplantation. Retrieved August 2, 2020 from https://www.who.int/topics/transplantation.Google ScholarGoogle Scholar
  59. [59] 2020. CPRA Calculator - OPTN. Retriefved from https://optn.transplant.hrsa.gov/resources/allocation-calculators/cpra-calculator/.Google ScholarGoogle Scholar
  60. [60] Kidney Allocation System - Professional Education - OPTN. Retrieved August 2, 2020 from https://optn.transplant.hrsa.gov/learn/professional-education/kidney-allocation-system/.Google ScholarGoogle Scholar
  61. [61] Organ Donation and Transplantation Statistics: Graph Data | Organ Donor. Retrieved August 2, 2020 from https://www.organdonor.gov/statistics-stories/statistics/data.html.Google ScholarGoogle Scholar
  62. [62] National Data - OPTN. Retrieved August 2, 2020 from https://optn.transplant.hrsa.gov/data/view-data-reports/national-data.Google ScholarGoogle Scholar
  63. [63] Living Donation Facts and Resources from UNOS | Living Donor Transplants. Retrieved March 20, 2021 from https://unos.org/transplant/living-donation/.Google ScholarGoogle Scholar
  64. [64] How we match organs - UNOS. Retrieved August 2, 2020 from https://unos.org/transplant/how-we-match-organs/.Google ScholarGoogle Scholar
  65. [65] EPTS Calculator - OPTN. Retrieved August 2, 2020 from https://optn.transplant.hrsa.gov/resources/allocation-calculators/epts-calculator/.Google ScholarGoogle Scholar
  66. [66] KDPI Calculator - OPTN. Retrieved August 2, 2020 from https://optn.transplant.hrsa.gov/resources/allocation-calculators/kdpi-calculator/.Google ScholarGoogle Scholar

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