Work

A Multilayer Residual Dendritic Neural Model for Predicting Stroke Prognosis

Year: 2025

Type: article

Abstract: Stroke, caused by occlusion or rupture of cerebral blood vessels, is a leading cause of disability and death globally. Accurate stroke prognosis can enhance clinical decisions and rehabilitation strategies. The dendritic neural model (DNM), inspired by biological neurons, shows strong predictive capability, but struggles with real-world small-scale tabular stroke data. Therefore, an improved residual dendritic neural model (RDNM) is proposed. It contains a series of stacked synaptic and dendritic layers to enhance the power. Residual connections are added between layers to address the vanishing gradient problem. Evaluations using one public and two private stroke prognosis datasets demonstrate that RDNM significantly outperforms original DNM and state-of-the-art deep-learning methods, highlighting its potential for clinical applications. Source code is available at https://github.com/jhc050998/RDNM.

Source: IEEE Access

DOI: 10.1109/access.2025.3591799

Authors: Yuxi Wang, Haochang Jin, Maocheng Cao, Xiong Xiao, Li Wang

Institutions: The Affiliated Taizhou People's Hospital of Nanjing Medical University, Nanjing Medical University, Shenzhen University, Shenzhen Institute of Information Technology

Language: English

Topic: Artificial Intelligence in Healthcare

Subfield: Health Information Management

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,075