ClinMamba: Leveraging state–space modeling for medication prediction in Electronic Medical Records
Year: 2025
Type: article
Abstract: With the growth of Electronic Medical Records (EMRs), extracting clinical knowledge for precise diagnosis and treatment is crucial in smart healthcare. However, drug prescription prediction models struggle with multi-modal information fusion, temporal dependency, and class imbalance, limiting their effectiveness in capturing the complexities of patient care. To address these challenges, we introduce ClinMamba, a multi-modal drug recommendation model that combines state–space modeling, sequence learning, and channel attention mechanisms. ClinMamba enhances the joint modeling of patients’ historical behaviors and static attributes. The model features a state–space-based LocalMamba module to capture the local dynamic features within medication time series, while leveraging ResNet and LSTM architectures to encode static information. Additionally, the Cross-Fusion module is incorporated to bolster inter-modal interactions, and the ECA attention mechanism selectively amplifies crucial channels, culminating in multi-label prescription predictions. We evaluate the model on the extensive ICU dataset MIMIC-III, which includes ten typical critical care diagnostic tasks. The experimental results demonstrate that ClinMamba significantly outperforms existing state-of-the-art models across several key metrics, including AUROC and AUPR, showcasing superior predictive power, robustness, and generalization capacity.
Source: Alexandria Engineering Journal
Authors: Yongwei Liu, Yuguang Wang, Si Ji, Yong Jiang
Institution: Jinzhou Medical University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,900
OpenAlex ID: W4415452487