English(EN) DIGOM: Disentangled Interaction Graphs for Outcome prediction in Multimodal healthcare

新的DIGOM框架增强了多模态医疗结果预测能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-26 04:00

研究人员开发了DIGOM，一个新颖的基于图的框架，旨在改进多模态医疗数据中的结果预测。DIGOM显式地建模了三种类型的交互：患者内跨模态、跨患者同模态以及跨患者跨模态。该框架学习一个具有解耦视图的稀疏多路图，实现端到端学习并选择信息丰富的关系。在MIMIC-IV和NIH-TB数据集上的五个临床预测任务中进行评估，DIGOM取得了最高的AUROC分数，同时证明了其对缺失模态的鲁棒性。 AI

影响该框架可以通过更好地利用复杂的患者数据来提高医疗保健的诊断准确性和治疗规划。

排序理由该集群包含一篇详细介绍新模型及其在医疗保健数据集上评估的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Hakima Laribi, Olivier Lefebvre, Dan Poenaru, Martin Valli\`eres · 2026-10-26 04:00

DIGOM：多模态医疗结果预测中的解耦交互图

arXiv:2610.32451v1 Announce Type: cross Abstract: Multimodal healthcare data contain relational information that extends beyond individual patient observations. Effective clinical outcome prediction therefore requires not only integrating information from multiple modalities, but…

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DIGOM：多模态医疗结果预测中的解耦交互图

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