Adaptively Weighted Top-N Recommendation for Organ Matching
Article No.: 9, Pages 1 - 29
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]
G. Adomavicius and A. Tuzhilin. 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), 734–749. DOI:https://doi.org/10.1109/TKDE.2005.99
[2]
Sahar Ahmadvand and Mir Saman Pishvaee. 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), 587–603. DOI:https://doi.org/10.1007/s10729-017-9414-6
[3]
I. Ashlagi, D. S. Gilchrist, A. E. Roth, and M. A. Rees. 2011. Nonsimultaneous chains and dominos in kidney-paired donation—Revisited. American Journal of Transplantation 11, 5 (2011), 984–994. DOI:https://doi.org/10.1111/j.1600-6143.2011.03481.x
[4]
Dimitris Bertsimas, Vivek F. Farias, and Nikolaos Trichakis. 2013. Fairness, efficiency, and flexibility in organ allocation for kidney transplantation. Operations Research 61, 1 (February 2013), 73–87. DOI:https://doi.org/10.1287/opre.1120.1138
[5]
Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, and Jonathan Eckstein. 2011. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends® in Machine Learning 3, 1 (January 2011), 1–122. DOI:https://doi.org/10.1561/2200000016
[6]
Jian-Feng Cai, Emmanuel J. Candès, and Zuowei Shen. 2010. A singular value thresholding algorithm for matrix completion. SIAM Journal on Optimization 20, 4 (January 2010), 1956–1982. DOI:https://doi.org/10.1137/080738970
[7]
Emmanuel Candès and Benjamin Recht. 2012. Exact matrix completion via convex optimization. Communications of the ACM 55, 6 (June 2012), 111–119. DOI:https://doi.org/10.1145/2184319.2184343
[8]
Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, and Hang Li. 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, 129–136. DOI:https://doi.org/10.1145/1273496.1273513
[9]
Xioayu Chen, Nathan Lau, and Ran Jin. 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.
[10]
Yao Cheng, Liang Yin, and Yong Yu. 2014. Low rank sparse linear methods for top-n recommendations. In 2014 IEEE International Conference on Data Mining, 90–99. DOI:https://doi.org/10.1109/ICDM.2014.112
[11]
Paolo Cremonesi, Yehuda Koren, and Roberto Turrin. 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, 39–46. DOI:https://doi.org/10.1145/1864708.1864721
[12]
Bruce Croft, Donald Metzler, and Trevor Strohman. 2009. Search Engines: Information Retrieval in Practice (1st ed.). Addison-Wesley Publishing Company, USA.
[13]
Israel David and Uri Yechiali. 1995. One-attribute sequential assignment match processes in discrete time. Operations Research 43, 5 (October 1995), 879–884. DOI:https://doi.org/10.1287/opre.43.5.879
[14]
Mukund Deshpande and George Karypis. 2004. Item-based top-N recommendation algorithms. ACM Transactions on Information Systems 22, 1 (January 2004), 143–177. DOI:https://doi.org/10.1145/963770.963776
[15]
G. Dongping, L. Yang, S. Xiaobei, X. Junxiang, Y. Yuan, and C. Hui. 2013. Long-term factors influencing survival after kidney transplantation. Transplantation Proceedings 45, 1 (January 2013), 129–133. DOI:https://doi.org/10.1016/j.transproceed.2012.08.014
[16]
David L. Donoho and Iain M. Johnstone. 1994. Ideal spatial adaptation by wavelet shrinkage. Biometrika 81, 3 (September 1994), 425–455. DOI:https://doi.org/10.1093/biomet/81.3.425
[17]
Asela Gunawardana and Guy Shani. 2015. Evaluating recommender systems. In Recommender Systems Handbook, Francesco Ricci, Lior Rokach and Bracha Shapira (eds.). Springer US, Boston, MA, 265–308. DOI:https://doi.org/10.1007/978-1-4899-7637-6_8
[18]
Emin Gundogar, Fatih M. Duran, Yavuz B. Canbolat, and Aydn Turkmen. 2005. Fuzzy organ allocation system for cadaveric kidney transplantation. Transplantation 80, 12 (December 2005), 1648–1653. DOI:https://doi.org/10.1097/01.tp.0000183287.04630.05
[19]
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: A factorization-machine based neural network for CTR prediction. (March 2017). Retrieved March 26, 2021 from http://arxiv.org/abs/1703.04247v1
[20]
Ke Guo, Deren Han, David Z. W. Wang, and Tingting Wu. 2017. Convergence of ADMM for multi-block nonconvex separable optimization models. Frontiers of Mathematics in China 12, 5 (October 2017), 1139–1162. DOI:https://doi.org/10.1007/s11464-017-0631-6
[21]
Trevor Hastie, Robert Tibshirani, and Martin Wainwright. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. CRC Press.
[22]
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 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, 173–182. DOI:https://doi.org/10.1145/3038912.3052569
[23]
Jonathan L. Herlocker, Joseph A. Konstan, Al Borchers, and John Riedl. 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, 230–237. DOI:https://doi.org/10.1145/312624.312682
[24]
Thomas Hofmann. 2004. Latent semantic models for collaborative filtering. ACM Transactions on Information Systems 22, 1 (January 2004), 89–115. DOI:https://doi.org/10.1145/963770.963774
[25]
Prateek Jain, Praneeth Netrapalli, and Sujay Sanghavi. 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, 665–674. DOI:https://doi.org/10.1145/2488608.2488693
[26]
Ran Jin, Xinwei Deng, Xiaoyu Chen, Liang Zhu, and Jun Zhang. 2019. Dynamic quality-process model in consideration of equipment degradation. 51, 3 (July 2019), 217–229.
[27]
Yehuda Koren. 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
[28]
Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 42, 8 (August 2009), 30–37. DOI:https://doi.org/10.1109/MC.2009.263
[29]
J. A. Nelder and R. W. M. Wedderburn. 1972. Generalized linear models. Journal of the Royal Statistical Society: Series A (General) 135, 3 (1972), 370–384.
[30]
Xia Ning and George Karypis. 2011. Sparse linear methods for top-n recommender systems. In 2011 IEEE 11th International Conference on Data Mining, 497–506. DOI:https://doi.org/10.1109/ICDM.2011.134
[31]
M. Nosotti, A. Dell'Amore, D. Diso, T. Oggionni, S. Aliberti, E. Balestro, A. Bertani, M. Boffini, A. Lucianetti, L. Luzzi, G. Paone, P. Parigi, C. Pellegrini, A. Rocca, P. Rottoli, L. Santambrogio, M. Schiavon, P. Solidoro, P. Vitulo, and P. Tarsia. 2017. Selection of candidates for lung transplantation: The first Italian consensus statement. Transplantation Proceedings 49, 4 (May 2017), 702–706. DOI:https://doi.org/10.1016/j.transproceed.2017.02.026
[32]
C. Wright Pinson, Irene D. Feurer, Jerita L. Payne, Paul E. Wise, Shannon Shockley, and Theodore Speroff. 2000. Health-related quality of life after solid organ transplantation: A prospective, multiorgan cohort study. Annals of Surgery 232, 4 (October 2000), 597–607. Retrieved September 27, 2020 from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1421192/
[33]
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012. Bayesian personalized ranking from implicit feedback. arXiv:1205.2618 [cs, stat] (May 2012).
[34]
RFI. 2008. Kidney Allocation Concepts: Request for Information. OPTN/UNOS Kidney Transplantation Committee (2008).
[35]
Francesco Ricci, Lior Rokach, and Bracha Shapira. 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, 1–35.
[36]
Rhonda Righter. 1989. A resource allocation problem in a random environment. Operations Research 37, 2 (April 1989), 329–338. DOI:https://doi.org/10.1287/opre.37.2.329
[37]
Alvin E. Roth, Tayfun Sönmez, and M. Utku Ünver. 2004. Kidney exchange. The Quarterly Journal of Economics 119, 2 (May 2004), 457–488. DOI:https://doi.org/10.1162/0033553041382157
[38]
R. Jean Ruth, Leon Wyszewianski, and Gary Herline. 1985. Kidney transplantation: A simulation model for examining demand and supply. Management Science 31, 5 (March 1985), 515–526. DOI:https://doi.org/10.1287/mnsc.31.5.515
[39]
Ruslan Salakhutdinov and Andriy Mnih. 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, 1257–1264.
[40]
Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 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, 285–295. DOI:https://doi.org/10.1145/371920.372071
[41]
K. Schulte, V. Klasen, C. Vollmer, C. Borzikowsky, U. Kunzendorf, and T. Feldkamp. 2018. Analysis of the Eurotransplant Kidney Allocation Algorithm: How should we balance utility and equity? Transplant Proc 50, 10 (December 2018), 3010–3016. DOI:https://doi.org/10.1016/j.transproceed.2018.08.040
[42]
Dorry L. Segev, Sommer E. Gentry, Daniel S. Warren, Brigitte Reeb, and Robert A. Montgomery. 2005. Kidney paired donation and optimizing the use of live donor organs. JAMA 293, 15 (April 2005), 1883–1890. DOI:https://doi.org/10.1001/jama.293.15.1883
[43]
Harald Steck. 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, 213–220. DOI:https://doi.org/10.1145/2507157.2507160
[44]
Mark D. Stegall. 2010. The right kidney for the right recipient: The status of deceased donor kidney allocation reform. Seminars in Dialysis 23, 3 (2010), 248–252. DOI:https://doi.org/10.1111/j.1525-139X.2010.00723.x
[45]
Mark D. Stegall, Peter G. Stock, Kenneth Andreoni, John J. Friedewald, and Alan B. Leichtman. 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), 4–8. DOI:https://doi.org/10.1016/j.humimm.2016.08.008
[46]
Xiaoyuan Su and Taghi M. Khoshgoftaar. 2009. A survey of collaborative filtering techniques. Advances in Artificial Intelligence 2009, (January 2009), 4:2. DOI:https://doi.org/10.1155/2009/421425
[47]
Robert Tibshirani. 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological) 58, 1 (1996), 267–288. DOI:https://doi.org/10.1111/j.2517-6161.1996.tb02080.x
[48]
Allison Tong, Stephen Jan, Germaine Wong, Jonathan C. Craig, Michelle Irving, Steve Chadban, Alan Cass, Niamh Marren, and Kirsten Howard. 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/10.1186/1471-2369-13-18
[49]
Yining Wang, Liwei Wang, Yuanzhi Li, Di He, Tie-Yan Liu, and Wei Chen. 2013. A theoretical analysis of NDCG type ranking measures. arXiv:1304.6480 [cs, stat] (April 2013).
[50]
Yu Wang, Wotao Yin, and Jinshan Zeng. 2019. Global convergence of ADMM in nonconvex nonsmooth optimization. Journal of Scientific Computing 78, 1 (January 2019), 29–63. DOI:https://doi.org/10.1007/s10915-018-0757-z
[51]
Hugh R. Wilson and Jack D. Cowan. 1972. Excitatory and inhibitory interactions in localized populations of model neurons. Biophys J. 12, 1 (January 1972), 1–24.
[52]
R. A. Wolfe, K. P. McCullough, and A. B. Leichtman. 2009. Predictability of survival models for waiting list and transplant patients: calculating LYFT. American Journal of Transplantation 9, 7 (2009), 1523–1527. DOI:https://doi.org/10.1111/j.1600-6143.2009.02708.x
[53]
R. A. Wolfe, K. P. McCullough, D. E. Schaubel, J. D. Kalbfleisch, S. Murray, M. D. Stegall, and A. B. Leichtman. 2008. Calculating life years from transplant (LYFT): Methods for kidney and kidney-pancreas candidates. American Journal of Transplantation 8, 4p2 (2008), 997–1011. DOI:https://doi.org/10.1111/j.1600-6143.2008.02177.x
[54]
Stephen J. Wright. 2015. Coordinate descent algorithms. arXiv:1502.04759 [math] (February 2015).
[55]
Junfeng Yang and Xiaoming Yuan. 2013. Linearized augmented Lagrangian and alternating direction methods for nuclear norm minimization. Math. Comp. 82, 281 (2013), 301–329. DOI:https://doi.org/10.1090/S0025-5718-2012-02598-1
[56]
Gui-Bo Ye and Xiaohui Xie. 2011. Split Bregman method for large scale fused Lasso. Computational Statistics & Data Analysis 55, 4 (April 2011), 1552–1569. DOI:https://doi.org/10.1016/j.csda.2010.10.021
[57]
Stefanos A. Zenios. 2002. Optimal control of a paired-kidney exchange program. Management Science 48, 3 (March 2002), 328–342. DOI:https://doi.org/10.1287/mnsc.48.3.328.7732
[58]
2004. WHO | Transplantation. Retrieved August 2, 2020 from https://www.who.int/topics/transplantation.
[59]
2020. CPRA Calculator - OPTN. Retriefved from https://optn.transplant.hrsa.gov/resources/allocation-calculators/cpra-calculator/.
[60]
Kidney Allocation System - Professional Education - OPTN. Retrieved August 2, 2020 from https://optn.transplant.hrsa.gov/learn/professional-education/kidney-allocation-system/.
[61]
Organ Donation and Transplantation Statistics: Graph Data | Organ Donor. Retrieved August 2, 2020 from https://www.organdonor.gov/statistics-stories/statistics/data.html.
[62]
National Data - OPTN. Retrieved August 2, 2020 from https://optn.transplant.hrsa.gov/data/view-data-reports/national-data.
[63]
Living Donation Facts and Resources from UNOS | Living Donor Transplants. Retrieved March 20, 2021 from https://unos.org/transplant/living-donation/.
[64]
How we match organs - UNOS. Retrieved August 2, 2020 from https://unos.org/transplant/how-we-match-organs/.
[65]
EPTS Calculator - OPTN. Retrieved August 2, 2020 from https://optn.transplant.hrsa.gov/resources/allocation-calculators/epts-calculator/.
[66]
KDPI Calculator - OPTN. Retrieved August 2, 2020 from https://optn.transplant.hrsa.gov/resources/allocation-calculators/kdpi-calculator/.
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Publication History
Published: 15 October 2021
Accepted: 01 June 2021
Revised: 01 June 2021
Received: 01 May 2021
Published in HEALTH Volume 3, Issue 1
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