Survival prediction models since liver transplantation - comparisons between Cox models and machine learning techniques

Machine Learning for Survival Analysis

(2019) Ping Wang et al.   ACM COMPUTING SURVEYS

Machine learning in clinical practice: prospects and pitfalls

(2019) Ian A Scott et al.   MEDICAL JOURNAL OF AUSTRALIA

Standard errors and confidence intervals for variable importance in random forest regression, classification, and survival

(2018) Hemant Ishwaran et al.   STATISTICS IN MEDICINE

Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data

(2018) Travers Ching et al.   PLoS Computational Biology

Machine-Learning Algorithms Predict Graft Failure After Liver Transplantation

(2017) Lawrence Lau et al.   TRANSPLANTATION

Editorial: Artificial Neural Networks as Models of Neural Information Processing

(2017) Marcel van Gerven et al.   Frontiers in Computational Neuroscience

Machine Learning in Medicine

(2015) R. C. Deo   CIRCULATION

Use of artificial intelligence as an innovative donor-recipient matching model for liver transplantation: Results from a multicenter Spanish study

(2014) Javier Briceño et al.   JOURNAL OF HEPATOLOGY

Why Is Organ Transplantation Clinically Important?

(2013) J. M. Grinyo   Cold Spring Harbor Perspectives in Medicine

MissForest--non-parametric missing value imputation for mixed-type data

(2011) D. J. Stekhoven et al.   BIOINFORMATICS

High-Dimensional Variable Selection for Survival Data

(2010) Hemant Ishwaran et al.   JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION

Random survival forests

(2008) Hemant Ishwaran et al.   Annals of Applied Statistics