Deploying to gh-pages from @ ee15a44 🚀

data/raw.csv

@@ -118,3 +118,4 @@ Survival Prediction After Transarterial Chemoembolization for Hepatocellular Car
 DeepPseudo: Pseudo Value Based Deep Learning Models for Competing Risk Analysis,https://doi.org/10.1609/aaai.v35i1.16125,M. Rahman et al.,2021,DeepPseudo,???,???,no,no,FFNN,no,no,yes,no,no,no,yes,no,no,LRP,https://github.com/umbc-sanjaylab/DeepPseudo AAAI2021,"SEER, WIHS",1164 (WIHS),yes,no,no,PASS,,,,,,,,,,
 Pseudo value-based Deep Neural Networks for Multi-state Survival Analysis,https://arxiv.org/abs/2207.05291,M. Rahman and S. Purushotham ,2022,msPseudo,???,???,no,no,FFNN,no,no,yes,yes,no,no,no,no,no,no,no,"METABRIC, EBMT",1975 (METABRIC),yes,no,no,PASS,,,,,,,,,,
 Neural Fine-Gray: Monotonic neural networks for competing risks,https://proceedings.mlr.press/v209/jeanselme23a.html,V. Jeanselme et al.,2023,Neural Fine-Gray,other,Cumulative Incidence Function,no,yes,Monotonic NN,no,no,yes,no,no,no,yes,no,yes,no,https://github.com/Jeanselme/NeuralFineGray,"PBC, FRAMINGHAM, SEER",312 (PBC),yes,no,no,,,,,,,,,,,
+CR-SAVAE: A Parametric Method for Survival Analysis with Competing Risks,https://eurasip.org/Proceedings/Eusipco/Eusipco2024/pdfs/0001526.pdf,P. Apellániz et al.,2024,CR-SAVAE,parametric,parameters of differentiable time distribution,yes,no,VAE,no,no,yes,no,no,no,no,no,no,BP,https://github.com/Patricia-A-Apellaniz/cr-savae,"Melanoma, MGUS2, EBMT",205 (Melanoma),no,,could be applied to high dim and extended to multimodality due to using the VAE architecture,,