Semi-Supervised Fuzzy DBN-Based Broad Learning System for Forecasting ICU Admissions in Post-Transplant COVID-19 Patients

Xiao Zhang, Àngela Nebot

2024

Abstract

This paper introduces a novel semi-supervised neuro-fuzzy system to predict ICU admissions among post-COVID organ transplant recipients. Addressing the challenges of small sample sizes and lacking labels in organ transplantation, our study takes on these issues by proposing a DBN-Based Dual Manifold Regularized Fuzzy Broad Learning System (D-DMR-FBLS). This system utilizes the streamlined and flat architecture of the Broad Learning System (BLS), integrating Deep Belief Networks (DBN) and Takagi-Sugeno-Kang (TSK) systems to enhance representation learning capacities during the Unsupervised Training Phase (UTP). The system combines the strong feature learning capabilities of DBN with the powerful fuzzy rule extraction capacity of the TSK system, enhancing the model’s predictive performance and generalization capability. Moreover, we propose two types of graph-based manifold regularization, sample-based and feature-based, within this novel D-DMR-FBLS framework. Our method enhances its predictive ability by exploiting both the similarity among unlabeled and labeled patient samples, as well as the correlations between features within the fuzzy feature space. Employed to predict ICU admission risks in post-transplant COVID-19 patients, the method has demonstrated superior performance over existing methods, particularly in scenarios with limited samples and labels, thereby providing more accurate decision support for medical professionals in optimizing resource allocation for transplant patients.

Paper Citation

in Harvard Style

Zhang X. and Nebot À. (2024). Semi-Supervised Fuzzy DBN-Based Broad Learning System for Forecasting ICU Admissions in Post-Transplant COVID-19 Patients. In Proceedings of the 14th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH; ISBN 978-989-758-708-5, SciTePress, pages 415-422. DOI: 10.5220/0012856300003758

in Bibtex Style

@conference{simultech24,
author={Xiao Zhang and Àngela Nebot},
title={Semi-Supervised Fuzzy DBN-Based Broad Learning System for Forecasting ICU Admissions in Post-Transplant COVID-19 Patients},
booktitle={Proceedings of the 14th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH},
year={2024},
pages={415-422},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012856300003758},
isbn={978-989-758-708-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH
TI - Semi-Supervised Fuzzy DBN-Based Broad Learning System for Forecasting ICU Admissions in Post-Transplant COVID-19 Patients
SN - 978-989-758-708-5
AU - Zhang X.
AU - Nebot À.
PY - 2024
SP - 415
EP - 422
DO - 10.5220/0012856300003758
PB - SciTePress