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        <identifier>oai:figshare.com:article/34027599</identifier>
        <datestamp>2026-09-29T20:22:08Z</datestamp>
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          <dc:title>&lt;b&gt;Siamese-HeteroGNN&lt;/b&gt;&lt;b&gt;_&lt;/b&gt;&lt;b&gt; &lt;/b&gt;&lt;b&gt;Multimodal Graph Neural Network&lt;/b&gt;</dc:title>
          <dc:creator>Meiru Wu (25138863)</dc:creator>
          <dc:subject>Clinical sciences not elsewhere classified</dc:subject>
          <dc:subject>Echocardiographic</dc:subject>
          <dc:subject>graph neural network</dc:subject>
          <dc:description>&lt;p dir="ltr"&gt;This work contributes a patient-level heterogeneous graph modeling cross-modal relationships without concatenation, Siamese branches with contrastive loss, adaptive modality dropout with Monte Carlo uncertainty estimation, a SHapley Additive exPlanations (SHAP)-guided one-vs-rest nomogram, and ComBat-harmonized features validated on an independent held-out cohort built entirely on public data.&lt;/p&gt;</dc:description>
          <dc:date>2026-09-29T20:22:08Z</dc:date>
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          <dc:type>Media</dc:type>
          <dc:identifier>10.6084/m9.figshare.34027599.v1</dc:identifier>
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