Confidence intervals of survival predictions with neural networks trained on molecular data
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[1] Jasper Snoek,et al. Second opinion needed: communicating uncertainty in medical machine learning , 2021, npj Digital Medicine.
[2] Ørnulf Borgan,et al. Continuous and discrete-time survival prediction with neural networks , 2019, Lifetime Data Analysis.
[3] Jeremy Nixon,et al. Analyzing the role of model uncertainty for electronic health records , 2019, CHIL.
[4] J. Minna,et al. LCE: an open web portal to explore gene expression and clinical associations in lung cancer , 2018, Oncogene.
[5] Changhee Lee,et al. DeepHit: A Deep Learning Approach to Survival Analysis With Competing Risks , 2018, AAAI.
[6] Federico Rotolo,et al. Robust estimation of the expected survival probabilities from high-dimensional Cox models with biomarker-by-treatment interactions in randomized clinical trials , 2017, BMC Medical Research Methodology.
[7] Alex Kendall,et al. What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? , 2017, NIPS.
[8] Siegfried Wahl,et al. Leveraging uncertainty information from deep neural networks for disease detection , 2016, Scientific Reports.
[9] Purushottam W. Laud,et al. Nonparametric survival analysis using Bayesian Additive Regression Trees (BART) , 2016, Statistics in medicine.
[10] Benjamin Haibe-Kains,et al. MetaGxData: Clinically Annotated Breast, Ovarian and Pancreatic Cancer Datasets and their Use in Generating a Multi-Cancer Gene Signature , 2016, Scientific Reports.
[11] Trevor J. Hastie,et al. Confidence intervals for random forests: the jackknife and the infinitesimal jackknife , 2013, J. Mach. Learn. Res..
[12] Naomi S. Altman,et al. Points of significance: Importance of being uncertain , 2013, Nature Methods.
[13] Stef van Buuren,et al. MICE: Multivariate Imputation by Chained Equations in R , 2011 .
[14] M. Pencina,et al. On the C‐statistics for evaluating overall adequacy of risk prediction procedures with censored survival data , 2011, Statistics in medicine.
[15] R. Gentleman,et al. Independent filtering increases detection power for high-throughput experiments , 2010, Proceedings of the National Academy of Sciences.
[16] Ralf Bender,et al. Generating survival times to simulate Cox proportional hazards models , 2005, Statistics in medicine.
[17] E Graf,et al. Assessment and comparison of prognostic classification schemes for survival data. , 1999, Statistics in medicine.
[18] E. Biganzoli,et al. Feed forward neural networks for the analysis of censored survival data: a partial logistic regression approach. , 1998, Statistics in medicine.
[19] R. Tibshirani. The lasso method for variable selection in the Cox model. , 1997, Statistics in medicine.
[20] D Faraggi,et al. A neural network model for survival data. , 1995, Statistics in medicine.
[21] F ROSENBLATT,et al. The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.
[22] Paul-Henry Cournède,et al. On the Use of Neural Networks with Censored Time-to-Event Data , 2020, ISMCO.
[23] Elisa T. Lee,et al. Survival analysis in public health research. , 1997, Annual review of public health.
[24] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[25] B. Efron. Bootstrap Methods: Another Look at the Jackknife , 1979 .
[26] D.,et al. Regression Models and Life-Tables , 2022 .