Rampolla R. Lung transplantation: an overview of candidacy and outcomes. Ochsner J. 2014;14(4):641–8.
Google Scholar
Meyer KC. Recent advances in lung transplantation. F1000Res 2018, 7.
Studer SM, Levy RD, McNeil K, Orens JB. Lung transplant outcomes: a review of survival, graft function, physiology, health-related quality of life and cost-effectiveness. Eur Respir J. 2004;24(4):674–85.
Article
CAS
Google Scholar
Leard LE, Holm AM, Valapour M, Glanville AR, Attawar S, Aversa M, Campos SV, Christon LM, Cypel M, Dellgren G, et al. Consensus document for the selection of lung transplant candidates: an update from the International Society for Heart and Lung Transplantation. J Heart Lung Transplant. 2021;40(11):1349–79.
Article
Google Scholar
Bos S, Vos R, Van Raemdonck DE, Verleden GM. Survival in adult lung transplantation: where are we in 2020? Curr Opin Organ Transplant. 2020;25(3):268–73.
Article
Google Scholar
Levvey B, Keshavjee S, Cypel M, Robinson A, Erasmus M, Glanville A, Hopkins P, Musk M, Hertz M, McCurry K. Influence of lung donor agonal and warm ischemic times on early mortality: analyses from the ISHLT DCD Lung Transplant Registry. J Heart Lung Transplantation. 2019;38(1):26–34.
Article
Google Scholar
Medved D, Ohlsson M, Höglund P, Andersson B, Nugues P, Nilsson J. Improving prediction of heart transplantation outcome using deep learning techniques. Sci Rep. 2018;8(1):3613.
Article
Google Scholar
Sidey-Gibbons JAM, Sidey-Gibbons CJ. Machine learning in medicine: a practical introduction. BMC Med Res Methodol. 2019;19(1):64.
Article
Google Scholar
Shehab M, Abualigah L, Shambour Q, Abu-Hashem MA, Shambour MKY, Alsalibi AI, Gandomi AH. Machine learning in medical applications: a review of state-of-the-art methods. Comput Biol Med. 2022;145:105458.
Article
Google Scholar
Handelman GS, Kok HK, Chandra RV, Razavi AH, Lee MJ, Asadi H. eDoctor: machine learning and the future of medicine. J Intern Med. 2018;284(6):603–19.
Article
CAS
Google Scholar
Kumar CJ, Das PR. The diagnosis of ASD using multiple machine learning techniques. International Journal of Developmental Disabilities 2021:1–11.
Killian MO, Payrovnaziri SN, Gupta D, Desai D, He Z. Machine learning–based prediction of health outcomes in pediatric organ transplantation recipients. JAMIA. 2021;4(1):ooab008.
Google Scholar
Mark E, Goldsman D, Keskinocak P, Sokol J. Using machine learning to estimate survival curves for patients receiving an increased risk for disease transmission heart, liver, or lung versus waiting for a standard organ. Transpl Infect Dis. 2019;21(6):e13181.
Article
Google Scholar
Naruka V, Arjomandi Rad A, Subbiah Ponniah H, Francis J, Vardanyan R, Tasoudis P, Magouliotis DE, Lazopoulos GL, Salmasi MY, Athanasiou T. Machine learning and artificial intelligence in cardiac transplantation: A systematic review. Artificial Organs, n/a(n/a).
Ferrarese A, Sartori G, Orrù G, Frigo AC, Pelizzaro F, Burra P, Senzolo M. Machine learning in liver transplantation: a tool for some unsolved questions? Transpl Int. 2021;34(3):398–411.
Article
Google Scholar
Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ. 2009;339:b2535.
Article
Google Scholar
Institute JB. Joanna Briggs Institute reviewers’ manual: 2014 edition. Australia: The Joanna Briggs Institute 2014:88–91.
Qiao N. A systematic review on machine learning in sellar region diseases: quality and reporting items. Endocr Connect. 2019;8(7):952–60.
Article
Google Scholar
Hosseini-Baharanchi FS, Hajizadeh E, Baghestani AR, Najafizadeh K, Shafaghi S. Bronchiolitis obliterans syndrome and death in iranian lung transplant recipients: a bayesian competing risks analysis. Tanaffos. 2016;15(3):141–6.
Google Scholar
Pande A, Li L, Rajeswaran J, Ehrlinger J, Kogalur UB, Blackstone EH, Ishwaran H. Boosted Multivariate Trees for Longitudinal Data. Mach Learn. 2017;106(2):277–305.
Article
Google Scholar
Barbosa EJM Jr, Lanclus M, Vos W, Van Holsbeke C, De Backer W, De Backer J, Lee J. Machine learning algorithms utilizing quantitative CT features may predict eventual onset of Bronchiolitis Obliterans Syndrome after Lung Transplantation. Acad Radiol. 2018;25(9):1201–12.
Article
Google Scholar
Oztekin A, Al-Ebbini L, Sevkli Z, Delen D. A decision analytic approach to predicting quality of life for lung transplant recipients: a hybrid genetic algorithms-based methodology. Eur J Oper Res. 2018;266(2):639–51.
Article
Google Scholar
Braccioni F, Bottigliengo D, Ermolao A, Schiavon M, Loy M, Marchi MR, Gregori D, Rea F, Vianello A. Dyspnea, effort and muscle pain during exercise in lung transplant recipients: an analysis of their association with cardiopulmonary function parameters using machine learning. Respir Res. 2020;21(1):267.
Article
CAS
Google Scholar
Dueñas-Jurado JM, Gutiérrez PA, Casado-Adam A, Santos-Luna F, Salvatierra-Velázquez A, Cárcel S, Robles-Arista CJC, Hervás-Martínez C. New models for donor-recipient matching in lung transplantations. PLoS ONE. 2021;16(6):e0252148.
Article
Google Scholar
Shaish H, Ahmed FS, Lederer D, D’Souza B, Armenta P, Salvatore M, Saqi A, Huang S, Jambawalikar S, Mutasa S. Deep learning of computed Tomography virtual wedge resection for prediction of histologic usual interstitial pneumonitis. Ann Am Thorac Soc. 2021;18(1):51–9.
Article
Google Scholar
Stefanuto PH, Romano R, Rees CA, Nasir M, Thakuria L, Simon A, Reed AK, Marczin N, Hill JE. Volatile organic compound profiling to explore primary graft dysfunction after lung transplantation. Sci Rep. 2022;12(1):2053.
Article
CAS
Google Scholar
Su J, Li CX, Liu HY, Lian QY, Chen A, You ZX, Li K, Cai YH, Lin YX, Pan JB, et al. The Airway Microbiota Signatures of infection and rejection in lung transplant recipients. Microbiol Spectr. 2022;10(2):e0034421.
Article
Google Scholar
Zafar F, Hossain MM, Zhang Y, Dani A, Schecter M, Hayes D Jr, Macaluso M, Towe C, Morales DLS: Lung Transplantation Advanced Prediction Tool: Determining Recipient’s Outcome for a Certain Donor. Transplantation 2022.
Troiani JS, Carlin BP. Comparison of bayesian, classical, and heuristic approaches in identifying acute disease events in lung transplant recipients. Stat Med. 2004;23(5):803–24.
Article
Google Scholar
Oztekin A, Delen D, Kong Z. Predicting the graft survival for heart-lung transplantation patients: an integrated data mining methodology. Int J Med Informatics. 2009;78(12):e84–96.
Article
Google Scholar
Delen D, Oztekin A, Kong Z. A machine learning-based approach to prognostic analysis of thoracic transplantations. Artif Intell Med. 2010;49(1):33–42.
Article
Google Scholar
Oztekin A, Kong ZYJ, Delen D. Development of a structural equation modeling-based decision tree methodology for the analysis of lung transplantations. Decis Support Syst. 2011;51(1):155–66.
Article
Google Scholar
Barbosa EM Jr, Simpson S, Lee JC, Tustison N, Gee J, Shou H. Multivariate modeling using quantitative CT metrics may improve accuracy of diagnosis of bronchiolitis obliterans syndrome after lung transplantation. Comput Biol Med. 2017;89:275–81.
Article
Google Scholar
Sithamparanathan S, Thirugnanasothy L, Clark S, Dark JH, Fisher AJ, Gould KF, Hasan A, Lordan JL, Meachery G, Parry G, et al. Observational study of lung transplant recipients surviving 20 years. Respir Med. 2016;117:103–8.
Article
Google Scholar
Chen-Yoshikawa TF. Ischemia-Reperfusion Injury in Lung Transplantation. Cells. 2021;10(6):1333.
Article
Google Scholar
Briceño J, Ciria R, de la Mata M. Donor-recipient matching: myths and realities. J Hepatol. 2013;58(4):811–20.
Article
Google Scholar
Demir A, Coosemans W, Decaluwé H, De Leyn P, Nafteux P, Van Veer H, Verleden GM, Van Raemdonck D. Donor-recipient matching in lung transplantation: which variables are important?†. Eur J Cardiothorac Surg. 2015;47(6):974–83.
Article
Google Scholar
Parulekar AD, Kao CC. Detection, classification, and management of rejection after lung transplantation. J Thorac Dis. 2019;11(Suppl 14):1732-s1739.
Google Scholar
Santana MJ, Feeny D, Jackson K, Weinkauf J, Lien D. Improvement in health-related quality of life after lung transplantation. Can Respir J. 2009;16(5):153–8.
Article
Google Scholar
Smeritschnig B, Jaksch P, Kocher A, Seebacher G, Aigner C, Mazhar S, Klepetko W. Quality of life after lung transplantation: a cross-sectional study. J Heart Lung Transplant. 2005;24(4):474–80.
Article
Google Scholar
Stącel T, Jaworska I, Zawadzki F, Wajda-Pokrontka M, Tatoj Z, Urlik M, Latos M, Szywacz W, Szczerba A, et al. Assessment of Quality of Life Among Patients After Lung Transplantation: A Single-Center Study. Transplant Proc. 2020;52(7):2165–72.
Article
Google Scholar
Kariv G, Shani V, Goldberg E, Leibovici L, Paul M. A model for diagnosis of pulmonary infections in solid-organ transplant recipients. Comput Methods Programs Biomed. 2011;104(2):135–42.
Article
Google Scholar
Leppke S, Leighton T, Zaun D, Chen S-C, Skeans M, Israni AK, Snyder JJ, Kasiske BL. Scientific Registry of Transplant recipients: collecting, analyzing, and reporting data on transplantation in the United States. Transplantation Reviews. 2013;27(2):50–6.
Article
Google Scholar
Guijo-Rubio D, Gutiérrez PA, Hervás-Martínez C. Machine learning methods in organ transplantation. Curr Opin Organ Transplant. 2020;25(4):399–405.
Article
Google Scholar
Balch JA, Delitto D, Tighe PJ, Zarrinpar A, Efron PA, Rashidi P, Upchurch GR Jr, Bihorac A, Loftus TJ. Machine learning applications in solid organ transplantation and related complications. Front Immunol. 2021;12:739728.
Article
CAS
Google Scholar
Shahmoradi L, Abtahi H, Amini S, Gholamzadeh M. Systematic review of using medical informatics in lung transplantation studies. Int J Med Informatics. 2020;136:104096.
Article
Google Scholar
Getz KD, He C, Li Y, Huang YV, Burstein DS, Rossano J, Aplenc R. Successful merging of data from the United Network for Organ sharing and the Pediatric Health Information System databases. Pediatr Transpl. 2018;22(5):e13168.
Article
Google Scholar
Massie AB, Kucirka LM, Segev DL. Big data in organ transplantation: registries and administrative claims. Am J Transplant. 2014;14(8):1723–30.
Article
CAS
Google Scholar
Thaler S, Menkovski V. The role of deep learning in improving healthcare. In: Data Science for Healthcare. edn.: Springer; 2019. pp. 75–116.
Subramanian J, Simon R. Overfitting in prediction models – is it a problem only in high dimensions? Contemp Clin Trials. 2013;36(2):636–41.
Article
Google Scholar
Alelyani S. Stable bagging feature selection on medical data. J Big Data. 2021;8(1):1–18.
Article
Google Scholar
Bagherzadeh-Khiabani F, Ramezankhani A, Azizi F, Hadaegh F, Steyerberg EW, Khalili D. A tutorial on variable selection for clinical prediction models: feature selection methods in data mining could improve the results. J Clin Epidemiol. 2016;71:76–85.
Article
Google Scholar
Sutton RT, Pincock D, Baumgart DC, Sadowski DC, Fedorak RN, Kroeker KI. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med. 2020;3(1):1–10.
Article
Google Scholar
Murphy EV. Clinical decision support: effectiveness in improving quality processes and clinical outcomes and factors that may influence success. Yale J Biol Med. 2014;87(2):187.
Google Scholar