{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T05:17:15Z","timestamp":1777267035647,"version":"3.51.4"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T00:00:00Z","timestamp":1657497600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T00:00:00Z","timestamp":1657497600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Solid-organ transplantation is a life-saving treatment for end-stage organ disease in highly selected patients. Alongside the tremendous progress in the last several decades, new challenges have emerged. The growing disparity between organ demand and supply requires optimal patient\/donor selection and matching. Improvements in long-term graft and patient survival require data-driven diagnosis and management of post-transplant complications. The growing abundance of clinical, genetic, radiologic, and metabolic data in transplantation has led to increasing interest in applying machine-learning (ML) tools that can uncover hidden patterns in large datasets. ML algorithms have been applied in predictive modeling of waitlist mortality, donor\u2013recipient matching, survival prediction, post-transplant complications diagnosis, and prediction, aiming to optimize immunosuppression and management. In this review, we provide insight into the various applications of ML in transplant medicine, why these were used to evaluate a specific clinical question, and the potential of ML to transform the care of transplant recipients. 36 articles were selected after a comprehensive search of the following databases: Ovid MEDLINE; Ovid MEDLINE Epub Ahead of Print and In-Process &amp; Other Non-Indexed Citations; Ovid Embase; Cochrane Database of Systematic Reviews (Ovid); and Cochrane Central Register of Controlled Trials (Ovid). In summary, these studies showed that ML techniques hold great potential to improve the outcome of transplant recipients. Future work is required to improve the interpretability of these algorithms, ensure generalizability through larger-scale external validation, and establishment of infrastructure to permit clinical integration.<\/jats:p>","DOI":"10.1038\/s41746-022-00637-2","type":"journal-article","created":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T13:03:14Z","timestamp":1657544594000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":82,"title":["The promise of machine learning applications in solid organ transplantation"],"prefix":"10.1038","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3827-8262","authenticated-orcid":false,"given":"Neta","family":"Gotlieb","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6393-9064","authenticated-orcid":false,"given":"Amirhossein","family":"Azhie","sequence":"additional","affiliation":[]},{"given":"Divya","family":"Sharma","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1278-7712","authenticated-orcid":false,"given":"Ashley","family":"Spann","sequence":"additional","affiliation":[]},{"given":"Nan-Ji","family":"Suo","sequence":"additional","affiliation":[]},{"given":"Jason","family":"Tran","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9943-2692","authenticated-orcid":false,"given":"Ani","family":"Orchanian-Cheff","sequence":"additional","affiliation":[]},{"given":"Bo","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Anna","family":"Goldenberg","sequence":"additional","affiliation":[]},{"given":"Michael","family":"Chass\u00e9","sequence":"additional","affiliation":[]},{"given":"Heloise","family":"Cardinal","sequence":"additional","affiliation":[]},{"given":"Joseph Paul","family":"Cohen","sequence":"additional","affiliation":[]},{"given":"Andrea","family":"Lodi","sequence":"additional","affiliation":[]},{"given":"Melanie","family":"Dieude","sequence":"additional","affiliation":[]},{"given":"Mamatha","family":"Bhat","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,11]]},"reference":[{"key":"637_CR1","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1038\/nbt.3889","volume":"35","author":"S Giwa","year":"2017","unstructured":"Giwa, S. et al. The promise of organ and tissue preservation to transform medicine. Nat. Biotechnol. 35, 530\u2013542 (2017).","journal-title":"Nat. Biotechnol."},{"key":"637_CR2","doi-asserted-by":"publisher","first-page":"2321","DOI":"10.1111\/jgs.15583","volume":"66","author":"CE Haugen","year":"2018","unstructured":"Haugen, C. E. et al. National trends in liver transplantation in older adults. J. Am. Geriatrics Soc. 66, 2321\u20132326 (2018).","journal-title":"J. Am. Geriatrics Soc."},{"key":"637_CR3","doi-asserted-by":"publisher","first-page":"2608","DOI":"10.1111\/j.1600-6143.2012.04245.x","volume":"12","author":"M Abecassis","year":"2012","unstructured":"Abecassis, M. et al. Solid\u2010organ transplantation in older adults: current status and future research. Am. J. Transplant. 12, 2608\u20132622 (2012).","journal-title":"Am. J. Transplant."},{"key":"637_CR4","doi-asserted-by":"crossref","first-page":"175346661988007","DOI":"10.1177\/1753466619880078","volume":"13","author":"AB Mitchell","year":"2019","unstructured":"Mitchell, A. B. & Glanville, A. R. Lung transplantation: a review of the optimal strategies for referral and patient selection. Therapeutic Adv. respiratory Dis. 13, 1753466619880078 (2019).","journal-title":"Therapeutic Adv. respiratory Dis."},{"key":"637_CR5","doi-asserted-by":"publisher","first-page":"273","DOI":"10.12659\/AOT.913217","volume":"24","author":"Y Schwager","year":"2019","unstructured":"Schwager, Y. et al. Prediction of three-year mortality after deceased donor kidney transplantation in adults with pre-transplant donor and recipient variables. Ann. Transplant. 24, 273 (2019).","journal-title":"Ann. Transplant."},{"key":"637_CR6","doi-asserted-by":"publisher","first-page":"4438","DOI":"10.3748\/wjg.v22.i18.4438","volume":"22","author":"CC Jadlowiec","year":"2016","unstructured":"Jadlowiec, C. C. & Taner, T. Liver transplantation: current status and challenges. World J. Gastroenterol. 22, 4438 (2016).","journal-title":"World J. Gastroenterol."},{"key":"637_CR7","doi-asserted-by":"crossref","unstructured":"Ortega, F. Organ transplantation in the 21th century. In L\u00f3pez-Larrea, C., L\u00f3pez-V\u00e1zquez, A., Su\u00e1rez-\u00c1lvarez, B (eds) Stem Cell Transplantation 13\u201326 (Springer, 2012).","DOI":"10.1007\/978-1-4614-2098-9_2"},{"key":"637_CR8","first-page":"080601","volume":"23","author":"D Piao","year":"2018","unstructured":"Piao, D., Hawxby, A., Wright, H. & Rubin, E. M. Perspective review on solid-organ transplant: needs in point-of-care optical biomarkers. J. Biomed. Opt. 23, 080601 (2018).","journal-title":"J. Biomed. Opt."},{"key":"637_CR9","doi-asserted-by":"publisher","first-page":"a015636","DOI":"10.1101\/cshperspect.a015636","volume":"4","author":"M Tonsho","year":"2014","unstructured":"Tonsho, M., Michel, S., Ahmed, Z., Alessandrini, A. & Madsen, J. C. Heart transplantation: challenges facing the field. Cold Spring Harb. Perspect. Med. 4, a015636 (2014).","journal-title":"Cold Spring Harb. Perspect. Med."},{"key":"637_CR10","unstructured":"Mitchell, T. M. Learning M (The McGraw-Hill Companies. Inc, 1997)."},{"key":"637_CR11","doi-asserted-by":"publisher","first-page":"1347","DOI":"10.1056\/NEJMra1814259","volume":"380","author":"A Rajkomar","year":"2019","unstructured":"Rajkomar, A., Dean, J. & Kohane, I. Machine learning in medicine. N. Engl. J. Med. 380, 1347\u20131358 (2019).","journal-title":"N. Engl. J. Med."},{"key":"637_CR12","doi-asserted-by":"publisher","first-page":"723","DOI":"10.1097\/TP.0000000000003424","volume":"105","author":"KL Connor","year":"2021","unstructured":"Connor, K. L., O\u2019Sullivan, E. D., Marson, L. P., Wigmore, S. J. & Harrison, E. M. The future role of machine learning in clinical transplantation. Transplantation 105, 723\u2013735 (2021).","journal-title":"Transplantation"},{"key":"637_CR13","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1214\/08-AOAS169","volume":"2","author":"H Ishwaran","year":"2008","unstructured":"Ishwaran, H., Kogalur, U. B., Blackstone, E. H. & Lauer, M. S. Random survival forests. Ann. Appl. Stat. 2, 841\u2013860. (2008).","journal-title":"Ann. Appl. Stat."},{"key":"637_CR14","doi-asserted-by":"publisher","first-page":"2067","DOI":"10.1111\/ajt.15265","volume":"19","author":"EM Hsich","year":"2019","unstructured":"Hsich, E. M. et al. Variables of importance in the Scientific Registry of Transplant Recipients database predictive of heart transplant waitlist mortality. Am. J. Transplant. 19, 2067\u20132076 (2019).","journal-title":"Am. J. Transplant."},{"key":"637_CR15","doi-asserted-by":"crossref","unstructured":"Medved, D., Nugues, P. & Nilsson, J. Simulating the outcome of heart allocation policies using deep neural networks. in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 6141\u20136144 (IEEE, 2018).","DOI":"10.1109\/EMBC.2018.8513637"},{"key":"637_CR16","unstructured":"Sauthier, N. B. R., Carreir, F. M. & Chass\u00e9, M. Detection of Potential Organ Donors; An Automatic Approach on Temporal Data (Critical Care Canada Forum, 2020)."},{"key":"637_CR17","unstructured":"Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning (MIT Press, 2016)."},{"key":"637_CR18","unstructured":"Wright, R. E. Logistic Regression. In Grimm, L. G. & Yarnold P. R. (eds), Reading and Understanding Multivariate Statistics (pp. 217\u2013244). Washington DC: American Psychological Association (1995)."},{"key":"637_CR19","doi-asserted-by":"publisher","first-page":"e0196707","DOI":"10.1371\/journal.pone.0196707","volume":"13","author":"E Hamouda","year":"2018","unstructured":"Hamouda, E., El-Metwally, S. & Tarek, M. Ant Lion Optimization algorithm for kidney exchanges. PLoS ONE 13, e0196707 (2018).","journal-title":"PLoS ONE"},{"key":"637_CR20","doi-asserted-by":"publisher","first-page":"1020","DOI":"10.1016\/j.jhep.2014.05.039","volume":"61","author":"J Brice\u00f1o","year":"2014","unstructured":"Brice\u00f1o, J. et al. Use of artificial intelligence as an innovative donor-recipient matching model for liver transplantation: results from a multicenter Spanish study. J. Hepatol. 61, 1020\u20131028 (2014).","journal-title":"J. Hepatol."},{"key":"637_CR21","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1002\/lt.24870","volume":"24","author":"MD Ayll\u00f3n","year":"2018","unstructured":"Ayll\u00f3n, M. D. et al. Validation of artificial neural networks as a methodology for donor\u2010recipient matching for liver transplantation. Liver Transplant. 24, 192\u2013203 (2018).","journal-title":"Liver Transplant."},{"key":"637_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.artmed.2017.02.004","volume":"77","author":"M Dorado-Moreno","year":"2017","unstructured":"Dorado-Moreno, M. et al. Dynamically weighted evolutionary ordinal neural network for solving an imbalanced liver transplantation problem. Artif. Intell. Med. 77, 1\u201311 (2017).","journal-title":"Artif. Intell. Med."},{"key":"637_CR23","doi-asserted-by":"publisher","first-page":"1095","DOI":"10.1111\/ajt.15705","volume":"20","author":"AM Placona","year":"2020","unstructured":"Placona, A. M. et al. Can donor narratives yield insights? A natural language processing proof of concept to facilitate kidney allocation. Am. J. Transplant. 20, 1095\u20131104 (2020).","journal-title":"Am. J. Transplant."},{"key":"637_CR24","doi-asserted-by":"crossref","unstructured":"Marrero, W. J., Lavieri, M. S., Guikema, S. D., Hutton, D. W. & Parikh, N. D. Development of a Predictive Model for Deceased Donor Organ Yield (LWW, 2018).","DOI":"10.1097\/TP.0000000000002274"},{"key":"637_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-21417-7","volume":"8","author":"D Medved","year":"2018","unstructured":"Medved, D. et al. Improving prediction of heart transplantation outcome using deep learning techniques. Sci. Rep. 8, 1\u20139 (2018).","journal-title":"Sci. Rep."},{"key":"637_CR26","doi-asserted-by":"publisher","first-page":"e0194985","DOI":"10.1371\/journal.pone.0194985","volume":"13","author":"J Yoon","year":"2018","unstructured":"Yoon, J. et al. Personalized survival predictions via trees of predictors: an application to cardiac transplantation. PLoS ONE 13, e0194985 (2018).","journal-title":"PLoS ONE"},{"key":"637_CR27","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1016\/j.cardfail.2019.01.018","volume":"25","author":"PE Miller","year":"2019","unstructured":"Miller, P. E. et al. Predictive abilities of machine learning techniques may be limited by dataset characteristics: insights from the UNOS database. J. Card. Fail. 25, 479\u2013483 (2019).","journal-title":"J. Card. Fail."},{"key":"637_CR28","doi-asserted-by":"publisher","first-page":"e0209068","DOI":"10.1371\/journal.pone.0209068","volume":"14","author":"E Mark","year":"2019","unstructured":"Mark, E., Goldsman, D., Gurbaxani, B., Keskinocak, P. & Sokol, J. Using machine learning and an ensemble of methods to predict kidney transplant survival. PLoS ONE 14, e0209068 (2019).","journal-title":"PLoS ONE"},{"key":"637_CR29","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc.: Ser. B (Methodol.) 58, 267\u2013288 (1996).","journal-title":"J. R. Stat. Soc.: Ser. B (Methodol.)"},{"key":"637_CR30","first-page":"18","volume":"2","author":"A Liaw","year":"2002","unstructured":"Liaw, A. & Wiener, M. Classification and regression by randomForest. R. N. 2, 18\u201322 (2002).","journal-title":"R. N."},{"key":"637_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-017-08008-8","volume":"7","author":"KD Yoo","year":"2017","unstructured":"Yoo, K. D. et al. A machine learning approach using survival statistics to predict graft survival in kidney transplant recipients: a multicenter cohort study. Sci. Rep. 7, 1\u201312. (2017).","journal-title":"Sci. Rep."},{"key":"637_CR32","doi-asserted-by":"crossref","unstructured":"Molinari, M. et al. Prediction of perioperative mortality of cadaveric liver transplant recipients during their evaluations. Transplantation 103, e297\u2013e307 (2019).","DOI":"10.1097\/TP.0000000000002810"},{"key":"637_CR33","doi-asserted-by":"crossref","unstructured":"Ershoff, B. D. et al. Training and validation of deep neural networks for the prediction of 90-day post-liver transplant mortality using Unos registry data. Transplantation Proc. 52, 246\u2013258 (2020).","DOI":"10.1016\/j.transproceed.2019.10.019"},{"key":"637_CR34","doi-asserted-by":"crossref","unstructured":"Khosravi, B., Pourahmad, S., Bahreini, A., Nikeghbalian, S. & Mehrdad, G. Five years survival of patients after liver transplantation and its effective factors by neural network and cox poroportional hazard regression models. Hepat. Mon. 15, e25164 (2015).","DOI":"10.5812\/hepatmon.25164"},{"key":"637_CR35","doi-asserted-by":"crossref","unstructured":"Raeisi Shahraki, H., Pourahmad, S. & Ayatollahi, S. M. T. Identifying the prognosis factors in death after liver transplantation via adaptive LASSO in Iran. J. Environ. Public Health 2016, 7620157 (2016).","DOI":"10.1155\/2016\/7620157"},{"key":"637_CR36","doi-asserted-by":"publisher","first-page":"775","DOI":"10.6002\/ect.2018.0170","volume":"17","author":"A Kazemi","year":"2019","unstructured":"Kazemi, A., Kazemi, K., Sami, A. & Sharifian, R. Identifying factors that affect patient survival after orthotopic liver transplant using machine-learning techniques. Exp. Clin. Transpl. 17, 775\u2013783 (2019).","journal-title":"Exp. Clin. Transpl."},{"key":"637_CR37","doi-asserted-by":"publisher","first-page":"e125","DOI":"10.1097\/TP.0000000000001600","volume":"101","author":"L Lau","year":"2017","unstructured":"Lau, L. et al. Machine-learning algorithms predict graft failure after liver transplantation. Transplantation 101, e125 (2017).","journal-title":"Transplantation"},{"key":"637_CR38","doi-asserted-by":"crossref","unstructured":"Zare, A. et al. A neural network approach to predict acute allograft rejection in liver transplant recipients using routine laboratory data. Hepatitis Monthly 17, (2017).","DOI":"10.5812\/hepatmon.55092"},{"key":"637_CR39","doi-asserted-by":"publisher","first-page":"277","DOI":"10.4258\/hir.2017.23.4.277","volume":"23","author":"L Tapak","year":"2017","unstructured":"Tapak, L., Hamidi, O., Amini, P. & Poorolajal, J. Prediction of kidney graft rejection using artificial neural network. Healthc. Inform. Res. 23, 277\u2013284 (2017).","journal-title":"Healthc. Inform. Res."},{"key":"637_CR40","doi-asserted-by":"publisher","first-page":"e0153355","DOI":"10.1371\/journal.pone.0153355","volume":"11","author":"JM Yabu","year":"2016","unstructured":"Yabu, J. M., Siebert, J. C. & Maecker, H. T. Immune profiles to predict response to desensitization therapy in highly HLA-sensitized kidney transplant candidates. PLoS ONE 11, e0153355 (2016).","journal-title":"PLoS ONE"},{"key":"637_CR41","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1023\/A:1018628609742","volume":"9","author":"JA Suykens","year":"1999","unstructured":"Suykens, J. A. & Vandewalle, J. Least squares support vector machine classifiers. Neural Process. Lett. 9, 293\u2013300 (1999).","journal-title":"Neural Process. Lett."},{"key":"637_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-42431-3","volume":"9","author":"H Abdeltawab","year":"2019","unstructured":"Abdeltawab, H. et al. A novel CNN-based CAD system for early assessment of transplanted kidney dysfunction. Sci. Rep. 9, 1\u201311. (2019).","journal-title":"Sci. Rep."},{"key":"637_CR43","doi-asserted-by":"publisher","first-page":"636","DOI":"10.1016\/j.healun.2019.01.1318","volume":"38","author":"MD Parkes","year":"2019","unstructured":"Parkes, M. D. et al. An integrated molecular diagnostic report for heart transplant biopsies using an ensemble of diagnostic algorithms. J. Heart Lung Transplant. 38, 636\u2013646 (2019).","journal-title":"J. Heart Lung Transplant."},{"key":"637_CR44","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1016\/j.healun.2019.01.1317","volume":"38","author":"KM Halloran","year":"2019","unstructured":"Halloran, K. M. et al. Molecular assessment of rejection and injury in lung transplant biopsies. J. Heart Lung Transplant. 38, 504\u2013513 (2019).","journal-title":"J. Heart Lung Transplant."},{"key":"637_CR45","doi-asserted-by":"publisher","first-page":"954","DOI":"10.1111\/ajt.15685","volume":"20","author":"K Halloran","year":"2020","unstructured":"Halloran, K. et al. Molecular phenotyping of rejection\u2010related changes in mucosal biopsies from lung transplants. Am. J. Transplant. 20, 954\u2013966 (2020).","journal-title":"Am. J. Transplant."},{"key":"637_CR46","doi-asserted-by":"publisher","first-page":"1600132","DOI":"10.1002\/prca.201600132","volume":"11","author":"KR Williams","year":"2017","unstructured":"Williams, K. R. et al. Use of a targeted urine proteome assay (TUPA) to identify protein biomarkers of delayed recovery after kidney transplant. PROTEOMICS\u2013Clin. Appl. 11, 1600132 (2017).","journal-title":"PROTEOMICS\u2013Clin. Appl."},{"key":"637_CR47","doi-asserted-by":"publisher","first-page":"e0228597","DOI":"10.1371\/journal.pone.0228597","volume":"15","author":"SD Costa","year":"2020","unstructured":"Costa, S. D. et al. The impact of deceased donor maintenance on delayed kidney allograft function: A machine learning analysis. PLoS ONE 15, e0228597 (2020).","journal-title":"PLoS ONE"},{"key":"637_CR48","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1097\/TP.0000000000000846","volume":"100","author":"C Villeneuve","year":"2016","unstructured":"Villeneuve, C. et al. Evolution and determinants of health-related quality-of-life in kidney transplant patients over the first 3 years after transplantation. Transplantation 100, 640\u2013647 (2016).","journal-title":"Transplantation"},{"key":"637_CR49","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1681\/ASN.2018070777","volume":"30","author":"O Aubert","year":"2019","unstructured":"Aubert, O. et al. Archetype analysis identifies distinct profiles in renal transplant recipients with transplant glomerulopathy associated with allograft survival. J. Am. Soc. Nephrol. 30, 625\u2013639 (2019).","journal-title":"J. Am. Soc. Nephrol."},{"key":"637_CR50","doi-asserted-by":"publisher","first-page":"1357","DOI":"10.1007\/s11548-018-1787-6","volume":"13","author":"S Moccia","year":"2018","unstructured":"Moccia, S. et al. Computer-assisted liver graft steatosis assessment via learning-based texture analysis. Int. J. computer Assist. Radiol. Surg. 13, 1357\u20131367 (2018).","journal-title":"Int. J. computer Assist. Radiol. Surg."},{"key":"637_CR51","doi-asserted-by":"crossref","unstructured":"Bhat, V., Tazari, M., Watt, K. D. & Bhat, M. New-onset diabetes and preexisting diabetes are associated with comparable reduction in long-term survival after liver transplant: a machine learning approach. Mayo Clinic Proc. 93, 1794\u20131802 (2018).","DOI":"10.1016\/j.mayocp.2018.06.020"},{"key":"637_CR52","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1007\/s00432-018-2589-5","volume":"144","author":"T Tanaka","year":"2018","unstructured":"Tanaka, T. & Voigt, M. D. Decision tree analysis to stratify risk of de novo non-melanoma skin cancer following liver transplantation. J. Cancer Res. Clin. Oncol. 144, 607\u2013615 (2018).","journal-title":"J. Cancer Res. Clin. Oncol."},{"key":"637_CR53","doi-asserted-by":"publisher","first-page":"1477","DOI":"10.1002\/hep.30478","volume":"69","author":"BP Lee","year":"2019","unstructured":"Lee, B. P. et al. Predicting low risk for sustained alcohol use after early liver transplant for acute alcoholic hepatitis: the sustained alcohol use post\u2013liver transplant score. Hepatology 69, 1477\u20131487 (2019).","journal-title":"Hepatology"},{"key":"637_CR54","doi-asserted-by":"publisher","first-page":"428","DOI":"10.3390\/jcm7110428","volume":"7","author":"H-C Lee","year":"2018","unstructured":"Lee, H.-C. et al. Prediction of acute kidney injury after liver transplantation: machine learning approaches vs. logistic regression model. J. Clin. Med. 7, 428 (2018).","journal-title":"J. Clin. Med."},{"key":"637_CR55","doi-asserted-by":"publisher","first-page":"21","DOI":"10.3389\/fnbot.2013.00021","volume":"7","author":"A Natekin","year":"2013","unstructured":"Natekin, A. & Knoll, A. Gradient boosting machines, a tutorial. Front. Neurorobotics 7, 21 (2013).","journal-title":"Front. Neurorobotics"},{"key":"637_CR56","doi-asserted-by":"publisher","first-page":"1201","DOI":"10.1016\/j.acra.2018.01.013","volume":"25","author":"EJM Barbosa Jr","year":"2018","unstructured":"Barbosa, E. J. M. Jr et al. Machine learning algorithms utilizing quantitative CT features may predict eventual onset of bronchiolitis obliterans syndrome after lung transplantation. Academic Radiol. 25, 1201\u20131212 (2018).","journal-title":"Academic Radiol."},{"key":"637_CR57","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12967-021-02990-4","volume":"19","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y. et al. An explainable supervised machine learning predictor of acute kidney injury after adult deceased donor liver transplantation. J. Transl. Med. 19, 1\u201315. (2021).","journal-title":"J. Transl. Med."},{"key":"637_CR58","doi-asserted-by":"publisher","first-page":"e14388","DOI":"10.1111\/ctr.14388","volume":"35","author":"PN Kampaktsis","year":"2021","unstructured":"Kampaktsis, P. N. et al. State\u2010of\u2010the\u2010art machine learning algorithms for the prediction of outcomes after contemporary heart transplantation: results from the UNOS database. Clin. Transplant. 35, e14388 (2021).","journal-title":"Clin. Transplant."},{"key":"637_CR59","doi-asserted-by":"publisher","first-page":"1230","DOI":"10.1097\/TP.0000000000002189","volume":"102","author":"EG Peyster","year":"2018","unstructured":"Peyster, E. G., Madabhushi, A. & Margulies, K. B. Advanced morphologic analysis for diagnosing allograft rejection: the case of cardiac transplant rejection. Transplantation 102, 1230 (2018).","journal-title":"Transplantation"},{"key":"637_CR60","doi-asserted-by":"crossref","unstructured":"Ribeiro, M. T., Singh, S. & Guestrin, C. \u201cWhy should I trust you?\u201d Explaining the predictions of any classifier. in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1135\u20131144 (ACM, 2016).","DOI":"10.1145\/2939672.2939778"},{"key":"637_CR61","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1007\/s10115-013-0679-x","volume":"41","author":"E \u0160trumbelj","year":"2014","unstructured":"\u0160trumbelj, E. & Kononenko, I. Explaining prediction models and individual predictions with feature contributions. Knowl. Inf. Syst. 41, 647\u2013665 (2014).","journal-title":"Knowl. Inf. Syst."},{"key":"637_CR62","unstructured":"Sundararajan, M., Taly, A. & Yan, Q. Axiomatic attribution for deep networks. in International Conference on Machine Learning. 3319\u20133328 (PMLR, 2017)."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-022-00637-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-022-00637-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-022-00637-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,28]],"date-time":"2024-09-28T22:12:53Z","timestamp":1727561573000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-022-00637-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,11]]},"references-count":62,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["637"],"URL":"https:\/\/doi.org\/10.1038\/s41746-022-00637-2","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,11]]},"assertion":[{"value":"23 November 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 June 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 July 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"M.B. received grants from Paladin, Novo Nordisk, Oncoustics, Natera, MedoAI, Lupin Speakers Bureau: Novartis, Paladin. The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"89"}}