Risk of Mortality Prediction Involving Time-Varying Covariates for Patients with Heart Failure Using Deep Learning.

ABSTRACT: Heart failure (HF) is challenging public medical and healthcare systems. This study aimed to develop and validate a novel deep learning-based prognostic model to predict the risk of all-cause mortality for patients with HF. We also compared the performance of the proposed model with those of classical deep learning- and traditional statistical-based models. The present study enrolled 730 patients with HF hospitalized at Toho University Ohashi Medical Center between April 2016 and March 2020. A recurrent neural network-based model (RNNSurv) involving time-varying covariates was developed and validated. The proposed RNNSurv showed better prediction performance than those of a deep feed-forward neural network-based model (referred as "DeepSurv") and a multivariate Cox proportional hazard mode

SUBMITTER: Nakamura K 

PROVIDER: S-EPMC9777280 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature