From f5e1931e89286b07018a9945b27c0a0ca0338f79 Mon Sep 17 00:00:00 2001 From: Vincent Jeanselme Date: Fri, 18 Aug 2023 19:10:36 +0100 Subject: [PATCH 1/3] Add Neural Fine Gray Add reference to Jeanselme's recent paper on modelling competing risks using monotonic neural networks. --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index b614bd3..81c47ff 100644 --- a/README.md +++ b/README.md @@ -70,6 +70,9 @@ Code: https://github.com/havakv/pycox 30. Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks by Nagpal et. al. 2021
Source: https://arxiv.org/abs/2003.01176 Code: https://autonlab.github.io/DeepSurvivalMachines/ +31. Neural Fine-Gray: Monotonic neural networks for competing risks by Jeanselme et. al. 20239
+Source: https://proceedings.mlr.press/v209/jeanselme23a.html
+Code: https://github.com/Jeanselme/NeuralFineGray ## Thesis 1. Gaussian Process Based From 865b49b276f00f3a56e17019ab7445e5b679cd63 Mon Sep 17 00:00:00 2001 From: Vincent Jeanselme Date: Fri, 18 Aug 2023 19:12:37 +0100 Subject: [PATCH 2/3] Correct numbering Space update to have 17. in list --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 81c47ff..0e2c688 100644 --- a/README.md +++ b/README.md @@ -38,7 +38,7 @@ Uncertainty-Aware Event Prediction by Hossein Soleimani, James Hensman, and Su Source: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8013802 16. Application of random survival forests in understanding the determinants of under-five child mortality in Uganda in the presence of covariates that satisfy the proportional and non-proportional hazards assumption by Justine B. Nasejje, Henry Mwambi. BMC Research Notes 2017
Source: https://bmcresnotes.biomedcentral.com/articles/10.1186/s13104-017-2775-6
-17.Posterior Consistency for a Non-parametric Survival Model under a Gaussian Process Prior by Tamara Fernández, Yee Whye Teh . 2016
+17. Posterior Consistency for a Non-parametric Survival Model under a Gaussian Process Prior by Tamara Fernández, Yee Whye Teh . 2016
Source: https://arxiv.org/abs/1611.02335 18. A comparison of the conditional inference survival forest model to random survival forests based on a simulation study as well as on two applications with time-to-event data byJustine B. NasejjeEmail author, Henry Mwambi, Keertan Dheda and Maia Lesosky. BMC Medical Research MethodologyBMC series – open, inclusive and trusted 2017
Source: https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-017-0383-8 From dadb44eb281746715b84fc8b264f137c124df3b0 Mon Sep 17 00:00:00 2001 From: Vincent Jeanselme Date: Fri, 18 Aug 2023 19:14:09 +0100 Subject: [PATCH 3/3] Correct date typo --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 0e2c688..2ec1a80 100644 --- a/README.md +++ b/README.md @@ -70,7 +70,7 @@ Code: https://github.com/havakv/pycox 30. Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks by Nagpal et. al. 2021
Source: https://arxiv.org/abs/2003.01176 Code: https://autonlab.github.io/DeepSurvivalMachines/ -31. Neural Fine-Gray: Monotonic neural networks for competing risks by Jeanselme et. al. 20239
+31. Neural Fine-Gray: Monotonic neural networks for competing risks by Jeanselme et. al. 2023
Source: https://proceedings.mlr.press/v209/jeanselme23a.html
Code: https://github.com/Jeanselme/NeuralFineGray