Cox-nnet v2.0: improved neural-network based survival prediction extended to large-scale EMR dataset

TL;DR

Cox-nnet v2.0 is an improved neural-network method for survival prediction that enhances efficiency and interpretability, making it suitable for large-scale EMR datasets and outperforming traditional models in accuracy.

Contribution

The paper introduces Cox-nnet v2.0, a significantly more efficient and interpretable neural-network based survival prediction method tailored for large-scale electronic medical records.

Findings

Reduces training time up to 32-fold on large datasets

Achieves statistically significant better prediction accuracy than Cox-PH

Provides permutation-based feature importance and coefficient directions

Abstract

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 electronic medical records (EMR) datasets. We also add permutation-based feature importance scores and the direction of feature coefficients. Applying on an EMR dataset of OPTN kidney transplantation, 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). Availability and implementation: Cox-nnet v2.0 is freely available to the public at https://github.com/lanagarmire/Cox-nnet-v2.0