TY - STD TI - Grinyó JM. Why is organ transplantation clinically important?Cold Spring Harb Perspect Med. 2013; 3(6). https://doi.org/10.1101/cshperspect.a014985. ID - ref1 ER - TY - JOUR AU - Merion, R. M. AU - Schaubel, D. E. AU - Dykstra, D. M. AU - Freeman, R. B. AU - Port, F. K. AU - Wolfe, R. A. PY - 2005 DA - 2005// TI - The survival benefit of liver transplantation JO - Am J Transplant VL - 5 UR - https://doi.org/10.1111/j.1600-6143.2004.00703.x DO - 10.1111/j.1600-6143.2004.00703.x ID - Merion2005 ER - TY - JOUR AU - Song, X. AU - Mitnitski, A. AU - Cox, J. AU - Rockwood, K. PY - 2004 DA - 2004// TI - Comparison of machine learning techniques with classical statistical models in predicting health outcomes JO - Stud Health Technol Inform VL - 107 ID - Song2004 ER - TY - JOUR AU - Deo, R. C. PY - 2015 DA - 2015// TI - Machine learning in medicine JO - Circulation VL - 132 UR - https://doi.org/10.1161/CIRCULATIONAHA.115.001593 DO - 10.1161/CIRCULATIONAHA.115.001593 ID - Deo2015 ER - TY - STD TI - Shailaja K, Seetharamulu B, Jabbar MA. Machine learning in healthcare: A review. In: Second International Conference on Electronics, Communication and Aerospace Technology (ICECA). Coimbatore: 2018. p. 910–4. https://doi.org/10.1109/ICECA.2018.8474918. ID - ref5 ER - TY - JOUR AU - Scott, I. A. AU - Cook, D. AU - Coiera, E. W. AU - Richards, B. PY - 2019 DA - 2019// TI - Machine learning in clinical practice: prospects and pitfalls JO - Med J Aust VL - 211 UR - https://doi.org/10.5694/mja2.50294 DO - 10.5694/mja2.50294 ID - Scott2019 ER - TY - JOUR AU - Desai, R. J. AU - Wang, S. V. AU - Vaduganathan, M. AU - Evers, T. AU - Schneeweiss, S. PY - 2020 DA - 2020// TI - Comparison of machine learning methods with traditional models for use of administrative claims with electronic medical records to predict heart failure outcomes JO - JAMA Netw open VL - 3 UR - https://doi.org/10.1001/jamanetworkopen.2019.18962 DO - 10.1001/jamanetworkopen.2019.18962 ID - Desai2020 ER - TY - JOUR AU - Cox, D. R. PY - 1972 DA - 1972// TI - Regression models and life-tables JO - J Roy Stat Soc Ser B Methodol VL - 34 ID - Cox1972 ER - TY - JOUR AU - Biganzoli, E. AU - Boracchi, P. AU - Mariani, L. AU - Marubini, E. PY - 1998 DA - 1998// TI - Feed forward neural networks for the analysis of censored survival data: a partial logistic regression approach JO - Stat Med VL - 17 UR - https://doi.org/3.0.CO;2-D DO - 3.0.CO;2-D ID - Biganzoli1998 ER - TY - STD TI - Wang P, Li Y, Reddy CK. Machine learning for survival analysis: A survey. ACM Comput Surv. 2019; 51(6). https://doi.org/10.1145/3214306. ID - ref10 ER - TY - JOUR AU - Xiang, A. AU - Lapuerta, P. AU - Ryutov, A. AU - Buckley, J. AU - Azen, S. PY - 2000 DA - 2000// TI - Comparison of the performance of neural network methods and cox regression for censored survival data JO - Comput Stat Data Anal VL - 34 UR - https://doi.org/10.1016/S0167-9473(99)00098-5 DO - 10.1016/S0167-9473(99)00098-5 ID - Xiang2000 ER - TY - JOUR AU - Faraggi, D. AU - Simon, R. PY - 1995 DA - 1995// TI - A neural network model for survival data JO - Stat Med VL - 14 UR - https://doi.org/10.1002/sim.4780140108 DO - 10.1002/sim.4780140108 ID - Faraggi1995 ER - TY - JOUR AU - Liestøl, K. AU - Andersen, P. K. AU - Andersen, U. PY - 1994 DA - 1994// TI - Survival analysis and neural nets JO - Stat Med VL - 13 UR - https://doi.org/10.1002/sim.4780131202 DO - 10.1002/sim.4780131202 ID - Liestøl1994 ER - TY - JOUR AU - Buckley, J. AU - James, I. PY - 1979 DA - 1979// TI - Linear regression with censored data JO - Biometrika VL - 66 UR - https://doi.org/10.1093/biomet/66.3.429 DO - 10.1093/biomet/66.3.429 ID - Buckley1979 ER - TY - JOUR AU - Lisboa, P. J. G. AU - Wong, H. AU - Harris, P. AU - Swindell, R. PY - 2003 DA - 2003// TI - A bayesian neural network approach for modelling censored data with an application to prognosis after surgery for breast cancer JO - Artif Intell Med VL - 28 UR - https://doi.org/10.1016/S0933-3657(03)00033-2 DO - 10.1016/S0933-3657(03)00033-2 ID - Lisboa2003 ER - TY - JOUR AU - Biganzoli, E. AU - Boracchi, P. AU - Marubini, E. PY - 2002 DA - 2002// TI - A general framework for neural network models on censored survival data JO - Neural Netw VL - 15 UR - https://doi.org/10.1016/S0893-6080(01)00131-9 DO - 10.1016/S0893-6080(01)00131-9 ID - Biganzoli2002 ER - TY - STD TI - Biglarian A, Bakhshi E, Baghestani AR, Gohari MR, Rahgozar M, Karimloo M. Nonlinear survival regression using artificial neural network. J Probab Stat. 2013; 2013. https://doi.org/10.1155/2013/753930. ID - ref17 ER - TY - JOUR AU - Jones, A. S. AU - Taktak, A. G. F. AU - Helliwell, T. R. AU - Fenton, J. E. AU - Birchall, M. A. AU - Husband, D. J. AU - Fisher, A. C. PY - 2006 DA - 2006// TI - An artificial neural network improves prediction of observed survival in patients with laryngeal squamous carcinoma JO - Eur Arch Otorhinolaryngol VL - 263 UR - https://doi.org/10.1007/s00405-006-0021-2 DO - 10.1007/s00405-006-0021-2 ID - Jones2006 ER - TY - JOUR AU - Taktak, A. AU - Antolini, L. AU - Aung, M. AU - Boracchi, P. AU - Campbell, I. AU - Damato, B. AU - Ifeachor, E. AU - Lama, N. AU - Lisboa, P. AU - Setzkorn, C. AU - Stalbovskaya, V. AU - Biganzoli, E. PY - 2007 DA - 2007// TI - Double-blind evaluation and benchmarking of survival models in a multi-centre study JO - Comput Biol Med VL - 37 UR - https://doi.org/10.1016/j.compbiomed.2006.10.001 DO - 10.1016/j.compbiomed.2006.10.001 ID - Taktak2007 ER - TY - STD TI - Blok JJ, Putter H, Metselaar HJ, Porte RJ, Gonella F, De Jonge J, Van den Berg AP, Van Der Zande J, De Boer JD, Van Hoek B, Braat AE. Identification and validation of the predictive capacity of risk factors and models in liver transplantation over time. Transplantation Direct. 2018; 4(9). https://doi.org/10.1097/TXD.0000000000000822. ID - ref20 ER - TY - JOUR AU - de Boer, J. D. AU - Putter, H. AU - Blok, J. J. AU - Alwayn, I. P. J. AU - van Hoek, B. AU - Braat, A. E. PY - 2019 DA - 2019// TI - Predictive capacity of risk models in liver transplantation JO - Transplantation Direct VL - 5 UR - https://doi.org/10.1097/TXD.0000000000000896 DO - 10.1097/TXD.0000000000000896 ID - de Boer2019 ER - TY - STD TI - R: A Language and Environment for Statistical Computing. http://www.R-project.org/. UR - http://www.R-project.org/ ID - ref22 ER - TY - STD TI - Kantidakis G, Lancia C, Fiocco M. Prediction Models for Liver Transplantation - Comparisons Between Cox Models and Machine Learning Techniques [abstract OC30-4]: 40th Annual Conference of the International Society for Clinical Biostatistics; 2019, pp. 343–4. https://kuleuvencongres.be/iscb40/images/iscb40-2019-e-versie.pdf. UR - https://kuleuvencongres.be/iscb40/images/iscb40-2019-e-versie.pdf ID - ref23 ER - TY - JOUR AU - Van Buuren, S. AU - Boshuizen, H. C. AU - Knook, D. L. PY - 1999 DA - 1999// TI - Multiple imputation of missing blood pressure covariates in survival analysis JO - Stat Med VL - 18 UR - https://doi.org/3.0.CO;2-R DO - 3.0.CO;2-R ID - Van Buuren1999 ER - TY - JOUR AU - Stekhoven, D. J. AU - Bühlmann, P. PY - 2012 DA - 2012// TI - Missforest-non-parametric missing value imputation for mixed-type data JO - Bioinformatics VL - 28 UR - https://doi.org/10.1093/bioinformatics/btr597 DO - 10.1093/bioinformatics/btr597 ID - Stekhoven2012 ER - TY - JOUR AU - Lawless, J. F. AU - Singhal, K. PY - 1978 DA - 1978// TI - Efficient screening of nonnormal regression models JO - Biometrics VL - 34 UR - https://doi.org/10.2307/2530022 DO - 10.2307/2530022 ID - Lawless1978 ER - TY - JOUR AU - Tibshirani, R. PY - 1997 DA - 1997// TI - The lasso method for variable selection in the cox model JO - Stat Med VL - 16 UR - https://doi.org/3.0.CO;2-3 DO - 3.0.CO;2-3 ID - Tibshirani1997 ER - TY - JOUR AU - Verweij, P. J. M. AU - Van Houwelingen, H. C. PY - 1993 DA - 1993// TI - Cross-validation in survival analysis JO - Stat Med VL - 12 UR - https://doi.org/10.1002/sim.4780122407 DO - 10.1002/sim.4780122407 ID - Verweij1993 ER - TY - JOUR AU - Ishwaran, H. AU - Kogalur, U. B. AU - Blackstone, E. H. AU - Lauer, M. S. PY - 2008 DA - 2008// TI - Random survival forests JO - Ann Appl Stat VL - 2 UR - https://doi.org/10.1214/08-AOAS169 DO - 10.1214/08-AOAS169 ID - Ishwaran2008 ER - TY - JOUR AU - Breiman, L. PY - 2001 DA - 2001// TI - Random forests JO - Mach Learn VL - 45 UR - https://doi.org/10.1023/A:1010933404324 DO - 10.1023/A:1010933404324 ID - Breiman2001 ER - TY - STD TI - Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Springer; 2009. https://doi.org/10.1007/978-0-387-84858-7. ID - ref31 ER - TY - JOUR AU - Segal, M. R. PY - 1988 DA - 1988// TI - Regression trees for censored data JO - Biometrics VL - 44 UR - https://doi.org/10.2307/2531894 DO - 10.2307/2531894 ID - Segal1988 ER - TY - JOUR AU - Hothorn, T. AU - Lausen, B. PY - 2003 DA - 2003// TI - On the exact distribution of maximally selected rank statistics JO - Comput Stat Data Anal VL - 43 UR - https://doi.org/10.1016/S0167-9473(02)00225-6 DO - 10.1016/S0167-9473(02)00225-6 ID - Hothorn2003 ER - TY - JOUR AU - van Gerven, M. AU - Bohte, S. PY - 2017 DA - 2017// TI - Editorial: Artificial neural networks as models of neural information processing JO - Front Comput Neurosci VL - 11 UR - https://doi.org/10.3389/fncom.2017.00114 DO - 10.3389/fncom.2017.00114 ID - van Gerven2017 ER - TY - BOOK AU - Minsky, M. AU - Papert, S. PY - 1969 DA - 1969// TI - Perceptrons; an Introduction to Computational Geometry. (Book edition 1) PB - MIT Press CY - Cambridge ID - Minsky1969 ER - TY - JOUR AU - Lapuerta, A. S. b. s. u. f. f. i. x. P. AU - L, L. PY - 1995 DA - 1995// TI - Use of neural networks in predicting the risk of coronary artery disease JO - Comput Biomed Res VL - 28 UR - https://doi.org/10.1006/cbmr.1995.1004 DO - 10.1006/cbmr.1995.1004 ID - Lapuerta1995 ER - TY - JOUR AU - Garson, G. D. PY - 1991 DA - 1991// TI - Interpreting neural network connection weights JO - AI Expert VL - 6 ID - Garson1991 ER - TY - JOUR AU - Harrell, F. E. AU - Lee, K. L. AU - Mark, D. B. PY - 1996 DA - 1996// TI - Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors JO - Stat Med VL - 15 UR - https://doi.org/3.0.CO;2-4 DO - 3.0.CO;2-4 ID - Harrell1996 ER - TY - JOUR AU - Van Houwelingen, J. C. AU - Le Cessie, S. PY - 1990 DA - 1990// TI - Predictive value of statistical models JO - Stat Med VL - 9 UR - https://doi.org/10.1002/sim.4780091109 DO - 10.1002/sim.4780091109 ID - Van Houwelingen1990 ER - TY - JOUR AU - Graf, E. AU - Schmoor, C. AU - Sauerbrei, W. AU - Schumacher, M. PY - 1999 DA - 1999// TI - Assessment and comparison of prognostic classification schemes for survival data JO - Stat Med VL - 18 UR - https://doi.org/3.0.CO;2-5 DO - 3.0.CO;2-5 ID - Graf1999 ER - TY - BOOK AU - Houwelingen, J. C. v. AU - Putter, H. PY - 2012 DA - 2012// TI - Dynamic Prediction in Clinical Survival Analysis. (Book edition 1) PB - CRC Press CY - Boca, Raton ID - Houwelingen2012 ER - TY - JOUR AU - Goh, A. T. C. PY - 1995 DA - 1995// TI - Back-propagation neural networks for modeling complex systems JO - Artif Intell Eng VL - 9 UR - https://doi.org/10.1016/0954-1810(94)00011-S DO - 10.1016/0954-1810(94)00011-S ID - Goh1995 ER - TY - JOUR AU - Olden, J. D. AU - Jackson, D. A. PY - 2002 DA - 2002// TI - Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks JO - Ecol Model VL - 154 UR - https://doi.org/10.1016/S0304-3800(02)00064-9 DO - 10.1016/S0304-3800(02)00064-9 ID - Olden2002 ER - TY - JOUR AU - Ishwaran, H. AU - Kogalur, U. B. AU - Gorodeski, E. Z. AU - Minn, A. J. AU - Lauer, M. S. PY - 2010 DA - 2010// TI - High-dimensional variable selection for survival data JO - J Am Stat Assoc VL - 105 UR - https://doi.org/10.1198/jasa.2009.tm08622 DO - 10.1198/jasa.2009.tm08622 ID - Ishwaran2010 ER - TY - JOUR AU - Ishwaran, H. AU - Lu, M. PY - 2019 DA - 2019// TI - Standard errors and confidence intervals for variable importance in random forest regression, classification, and survival JO - Stat Med VL - 38 UR - https://doi.org/10.1002/sim.7803 DO - 10.1002/sim.7803 ID - Ishwaran2019 ER - TY - JOUR AU - Schemper, M. AU - Smith, T. L. PY - 1996 DA - 1996// TI - A note on quantifying follow-up in studies of failure time JO - Control Clin Trials VL - 17 UR - https://doi.org/10.1016/0197-2456(96)00075-X DO - 10.1016/0197-2456(96)00075-X ID - Schemper1996 ER - TY - JOUR AU - Kaplan, E. L. AU - Meier, P. PY - 1958 DA - 1958// TI - Nonparametric estimation from incomplete observations JO - J Am Stat Assoc VL - 53 UR - https://doi.org/10.1080/01621459.1958.10501452 DO - 10.1080/01621459.1958.10501452 ID - Kaplan1958 ER - TY - JOUR AU - Lau, L. AU - Kankanige, Y. AU - Rubinstein, B. AU - Jones, R. AU - Christophi, C. AU - Muralidharan, V. AU - Bailey, J. PY - 2017 DA - 2017// TI - Machine-learning algorithms predict graft failure after liver transplantation JO - Transplant VL - 101 UR - https://doi.org/10.1097/TP.0000000000001600 DO - 10.1097/TP.0000000000001600 ID - Lau2017 ER - TY - JOUR AU - Briceño, J. AU - Cruz-Ramírez, M. AU - Prieto, M. AU - Navasa, M. AU - De Urbina, J. O. AU - Orti, R. AU - Gómez-Bravo, M. N. AU - Otero, A. AU - Varo, E. AU - Tomé, S. AU - Clemente, G. AU - Bañares, R. AU - Bárcena, R. AU - Cuervas-Mons, V. AU - Solórzano, G. AU - Vinaixa, C. AU - Rubín, N. AU - Colmenero, J. AU - Valdivieso, A. AU - Ciria, R. AU - Hervás-Martínez, C. AU - De La Mata, M. PY - 2014 DA - 2014// TI - Use of artificial intelligence as an innovative donor-recipient matching model for liver transplantation: Results from a multicenter spanish study JO - J Hepatol VL - 61 UR - https://doi.org/10.1016/j.jhep.2014.05.039 DO - 10.1016/j.jhep.2014.05.039 ID - Briceño2014 ER - TY - JOUR AU - Loh, W. -. Y. AU - Shih, Y. -. S. PY - 1997 DA - 1997// TI - Split selection methods for classification trees JO - Stat Sin VL - 7 ID - Loh1997 ER - TY - STD TI - Ching T, Zhu X, Garmire LX. Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data. PLoS Comput Biol. 2018; 14(4). https://doi.org/10.1371/journal.pcbi.1006076. ID - ref51 ER -