MULTIMODAL DEEP LEARNING FOR SEVERITY GRADING OF SYSTEMIC LUPUS ERYTHEMATOSUS USING CLINICAL AND LABORATORY DATA
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2025
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| author | J. Jayashree, Dr. T. Sree Kala |
| author_facet | J. Jayashree, Dr. T. Sree Kala |
| contents | <p><a href="https://ijetrm.com/issues/files/Dec-2025-19-1766138750-DEC50.pdf" target="_blank" rel="noopener">Systemic Lupus Erythematosus (SLE)</a> is a complex autoimmune disease characterized by heterogeneous clinical<br>manifestations and fluctuating disease severity. Accurate severity grading is essential for effective treatment planning<br>and disease management. Traditional diagnostic approaches often rely on limited clinical indicators and fail to capture<br>the complex interactions among diverse patient data. This study proposes a multimodal deep learning framework for<br>severity grading of SLE by integrating clinical features and laboratory test results. The proposed model employs<br>specialized neural network modules to extract complementary representations from each data modality and fuses them<br>using an attention-based mechanism to capture inter-modal relationships. To address the ordered nature of disease<br>severity levels, an ordinal learning strategy is incorporated to ensure consistent and clinically meaningful predictions.<br>Experimental evaluation on SLE patient datasets demonstrates that the multimodal approach significantly outperforms<br>unimodal models in terms of accuracy, robustness, and severity discrimination. The results highlight the effectiveness<br>of multimodal deep learning in capturing complex disease patterns and its potential to support intelligent clinical<br>decision-making for SLE management.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17986365 |
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| publishDate | 2025 |
| publisher | Zenodo |
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| spellingShingle | MULTIMODAL DEEP LEARNING FOR SEVERITY GRADING OF SYSTEMIC LUPUS ERYTHEMATOSUS USING CLINICAL AND LABORATORY DATA J. Jayashree, Dr. T. Sree Kala Systemic Lupus Erythematosus (SLE), Multimodal Deep Learning, Severity Grading, Clinical and Laboratory Data, Ordinal Learning, Attention Mechanism, Autoimmune Disease Prediction. <p><a href="https://ijetrm.com/issues/files/Dec-2025-19-1766138750-DEC50.pdf" target="_blank" rel="noopener">Systemic Lupus Erythematosus (SLE)</a> is a complex autoimmune disease characterized by heterogeneous clinical<br>manifestations and fluctuating disease severity. Accurate severity grading is essential for effective treatment planning<br>and disease management. Traditional diagnostic approaches often rely on limited clinical indicators and fail to capture<br>the complex interactions among diverse patient data. This study proposes a multimodal deep learning framework for<br>severity grading of SLE by integrating clinical features and laboratory test results. The proposed model employs<br>specialized neural network modules to extract complementary representations from each data modality and fuses them<br>using an attention-based mechanism to capture inter-modal relationships. To address the ordered nature of disease<br>severity levels, an ordinal learning strategy is incorporated to ensure consistent and clinically meaningful predictions.<br>Experimental evaluation on SLE patient datasets demonstrates that the multimodal approach significantly outperforms<br>unimodal models in terms of accuracy, robustness, and severity discrimination. The results highlight the effectiveness<br>of multimodal deep learning in capturing complex disease patterns and its potential to support intelligent clinical<br>decision-making for SLE management.</p> |
| title | MULTIMODAL DEEP LEARNING FOR SEVERITY GRADING OF SYSTEMIC LUPUS ERYTHEMATOSUS USING CLINICAL AND LABORATORY DATA |
| topic | Systemic Lupus Erythematosus (SLE), Multimodal Deep Learning, Severity Grading, Clinical and Laboratory Data, Ordinal Learning, Attention Mechanism, Autoimmune Disease Prediction. |
| url | https://doi.org/10.5281/zenodo.17986365 |