A Highly Nonlinear Survival Network for Hospital Readmission Prediction of Cardiac Patients

Yuejing Zhai, Yiping Li, Lihua He, Wuman Luo

2025

Abstract

Hospital readmission prediction of cardiac patients is an increasingly important survival analysis problem these days. So far, three groups of methods for cardiac readmission have been proposed: statistical-based, machine learning-based and deep learning-based. However, the assumptions of the statistical-based methods limit their practicality in real-world applications. The traditional machine learning-based methods suffer from the problem of over-reliance on feature engineering. Deep learning-based methods can be further classified into two groups in terms of how they deal with first hitting times: discrete strategy-based and continuous strategy-based. It is nontrivial for the discrete strategy-based methods to find the optimal granularity of output time intervals. The continuous strategy-based methods assume nonlinear proportional hazards condition, which often limits the model performance in practical applications. Besides, existing deep learning-based methods still have room for improvement in calculating the mean value of fitted dropout models. To address these issues, in this paper, we propose a highly nonlinear survival network called Environment-Aware Max-out Deep Survival Neural Network (EMaxSurv) to predict the risk value of hospital readmission of cardiac patients. EMaxSurv is based on a key observation that environmental conditions have a significant impact on the health of cardiac patients. The basic idea of EMaxSurv is to adopt maxout deep networks combined with environmental information to better capture the relationship between covariates and the distribution of the first-hitting times. To evaluate the proposed model, we conduct extensive experiments on three real world datasets. The experimental results show that EMaxSurv outperforms the other baselines in all three datasets.

Paper Citation

in Harvard Style

Zhai Y., Li Y., He L. and Luo W. (2025). A Highly Nonlinear Survival Network for Hospital Readmission Prediction of Cardiac Patients. In Proceedings of the 10th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS; ISBN 978-989-758-750-4, SciTePress, pages 183-190. DOI: 10.5220/0013195300003944

in Bibtex Style

@conference{iotbds25,
author={Yuejing Zhai and Yiping Li and Lihua He and Wuman Luo},
title={A Highly Nonlinear Survival Network for Hospital Readmission Prediction of Cardiac Patients},
booktitle={Proceedings of the 10th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS},
year={2025},
pages={183-190},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013195300003944},
isbn={978-989-758-750-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS
TI - A Highly Nonlinear Survival Network for Hospital Readmission Prediction of Cardiac Patients
SN - 978-989-758-750-4
AU - Zhai Y.
AU - Li Y.
AU - He L.
AU - Luo W.
PY - 2025
SP - 183
EP - 190
DO - 10.5220/0013195300003944
PB - SciTePress