ArticleCommunications medicine2026
Deep knowledge-driven multi-modal fusion for diagnosis and prognosis of SI-ARDS.
Article in Communications medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
23 authors.
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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.
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