MADMN: A Multi-modal Attention and Dynamic Memory Network for Early Mortality Risk Prediction in Electronic Medical Records
DOI:
https://doi.org/10.15837/ijccc.2025.3.6968Keywords:
Multimodal Data Analysis, Dynamic Memory Network, Cross-modal Attention, Electronic Medical Records, Risk PredictionAbstract
This paper proposes a novel Multi-modal Attention and Dynamic Memory Network (MADMN) model for early mortality risk prediction based on Electronic Medical Records (EMRs). The model integrates multi-modal feature extraction, cross-modal attention fusion, and dynamic memory networks within a unified framework to process structured, time-series, and textual data. MADMN effectively captures complex temporal dependencies and multimodal interactions, enhancing prediction accuracy and interpretability. Experimental results demonstrate that MADMN significantly outperforms traditional machine learning and deep learning baselines in terms of Accuracy, F1 Score, and ROC-AUC. Furthermore, SHAP analysis validates the model’s interpretability by highlighting key features contributing to predictions. The model also supports counterfactual analysis, enabling personalized treatment decisions and resource optimization. MADMN offers a robust and interpretable solution for multimodal medical data analysis and risk prediction, paving the way for advancements in precision medicine.
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Morid, M. A., Sheng, O. R. L., & Dunbar, J. (2023). Time series prediction using deep learning methods in healthcare. ACM Transactions on Management Information Systems, 14(1), 1-29.
Foersch, S., Glasner, C., Woerl, A. C., Eckstein, M., Wagner, D. C., Schulz, S., . . .& Jesinghaus, M. (2023). Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer. Nature Medicine, 29(2), 430-439.
Fremond, S., Andani, S., Wolf, J. B., Dijkstra, J., Melsbach, S., Jobsen, J. J., . . .& Bosse, T.
(2023). Interpretable deep learning model to predict the molecular classification of endometrial cancer. The Lancet Digital Health, 5(2), e71-e82.
Saeedi, S., Rezayi, S., Keshavarz, H., & R. Niakan Kalhori, S. (2023). MRI-based brain tumor detection using convolutional deep learning methods. BMC Medical Informatics and Decision Making, 23(1), 16.
Curila, S., Buciu, I., Grava, C., Trip, D. N., & Straciuc, O. M. (2024). COVID-19 lung infection segmentation using statistics and active contour. International Journal of Computers Communications & Control, 19(6). https://doi.org/10.15837/ijccc.2024.6.6862
Mahmood, M. A., Alsalem, K., Elbashir, M., El-Ghany, S. A., & Abd El-Aziz, A. (2024). Acute knee injury detection with MRI. International Journal of Computers Communications & Control, 19(5). https://doi.org/10.15837/ijccc.2024.5.6648
Vrbančič, G., Pečnik, Š., & Podgorelec, V. (2022). Hyper-parameter Optimization for COVID-19 X-ray Classification. Computer Science and Information Systems, 19(1), 327-352. https://doi.org/10.2298/CSIS210209056V
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Forrest, I. S., Petrazzini, B. O., . . .& Do, R. (2023). Marker for coronary artery disease validation. The Lancet, 401(10372), 215-225.
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