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For: Steingrimsson JA, Morrison S. Deep learning for survival outcomes. Stat Med 2020;39:2339-49. [PMID: 32281672 DOI: 10.1002/sim.8542] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
Number Citing Articles
1 Hu L, Ji J, Li F. Estimating heterogeneous survival treatment effect in observational data using machine learning. Stat Med 2021;40:4691-713. [PMID: 34114252 DOI: 10.1002/sim.9090] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
2 Zhao L. Deep Neural Networks For Predicting Restricted Mean Survival Times. Bioinformatics 2021:btaa1082. [PMID: 33399818 DOI: 10.1093/bioinformatics/btaa1082] [Reference Citation Analysis]
3 Billah ME, Javed F. Bayesian Convolutional Neural Network-based Models for Diagnosis of Blood Cancer. Applied Artificial Intelligence. [DOI: 10.1080/08839514.2021.2011688] [Reference Citation Analysis]
4 Utkin LV, Satyukov ED, Konstantinov AV. SurvNAM: The machine learning survival model explanation. Neural Netw 2021;147:81-102. [PMID: 34995952 DOI: 10.1016/j.neunet.2021.12.015] [Reference Citation Analysis]
5 Ganesan K, Pichai S, Kavitha MS, Takahashi M. Data imputation in deep neural network to enhance breast cancer detection. Int J Imaging Syst Tech. [DOI: 10.1002/ima.22743] [Reference Citation Analysis]