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Deep knowledge-driven multi-modal fusion for diagnosis and prognosis of SI-ARDS
Chen, Hongyi Gu, Yang Zhang, Guangwei Zhang, Yan Duan, Xiaohui Li, Ziying Lin, Jiaqi Yi, Xiaoling Chen, Mansheng Yang, Tianqi
Chen, Hongyi
Gu, Yang
Zhang, Guangwei
Zhang, Yan
Duan, Xiaohui
Li, Ziying
Lin, Jiaqi
Yi, Xiaoling
Chen, Mansheng
Yang, Tianqi
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Department
Machine Learning
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Journal article
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http://creativecommons.org/licenses/by/4.0/
Language
English
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Abstract
BackgroundSepsis-Induced Acute Respiratory Distress Syndrome (SI-ARDS) presents significant diagnostic and prognostic challenges due to its complex clinical manifestations and high mortality rate.MethodsWe developed a deep Knowledge-Driven multi-Modal Fusion (KDMF) framework for the accurate diagnosis and prognosis of SI-ARDS. The model leverages multi-modal data, including CT images, CT reports, and laboratory indicators, alongside a disease-specific knowledge graph.ResultsKDMF achieves superior performance in predicting SI-ARDS incidence (AUC 0.930) and time to 28-day mortality (AUC 0.843, C-index 0.833). Comprehensive error analysis and ablation studies demonstrate the critical contributions of each data modality and the integrated knowledge graph.ConclusionsThe results highlight the potential of KDMF to enhance early intervention and treatment strategies, underscoring the robustness and interpretability of the framework in clinical applications.
Citation
H. Chen, Y. Gu, G. Zhang, Y. Zhang, X. Duan, Z. Li , et al., "Deep knowledge-driven multi-modal fusion for diagnosis and prognosis of SI-ARDS," Communications Medicine, 2026, https://doi.org/10.1038/s43856-026-01638-y.
Source
Communications Medicine
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Keywords
32 Biomedical and Clinical Sciences, 3202 Clinical Sciences, 3 Good Health and Well Being
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Publisher
Springer Nature