Zhuya Huang

Accurate prediction of gestational diabetes mellitus (GDM) is critical for improving maternal and fetal outcomes. This study develops a Transformer-based multimodal fusion model that integrates tabular clinical features and image-encoded electronic health records (EHRs), aiming for accurate end-to-end classification of GDM. Preprocessed EHRs were transformed into grayscale, RGB, and heatmap, with visual features were extracted by a Vision Transformer and tabular features by an MLP.

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