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ABSTRACT: Summary
Cox-nnet is a neural-network based prognosis prediction method, originally applied to genomics data. Here we propose the version 2 of Cox-nnet, with significant improvement on efficiency and interpretability, making it suitable to predict prognosis based on large-scale population data, including those electronic medical records (EMR) datasets. We also add permutation-based feature importance scores and the direction of feature coefficients. When applied on a kidney transplantation dataset, Cox-nnet v2.0 reduces the training time of Cox-nnet up to 32 folds (n = 10,000) and achieves better prediction accuracy than Cox-PH (p < 0.05). It also achieves similarly superior performance on a publicly available SUPPORT data (n = 8,000). The high efficiency and accuracy make Cox-nnet v2.0 a desirable method for survival prediction in large-scale EMR data.Availability and implementation
Cox-nnet v2.0 is freely available to the public at https://github.com/lanagarmire/Cox-nnet-v2.0.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Wang D
PROVIDER: S-EPMC8428611 | biostudies-literature |
REPOSITORIES: biostudies-literature