DIGOM: Disentangled Interaction Graphs for Outcome prediction in Multimodal healthcare
Read the original on arXiv AI →DIGOM (Disentangled Interaction Graphs for Outcome prediction in Multimodal healthcare) is a graph-based framework that jointly models intra‑patient cross‑modal, cross‑patient same‑modal, and cross‑patient cross‑modal interactions. It learns a sparse multiplex graph with patient‑adaptive connectivity through a differentiable sampling mechanism, enabling end‑to‑end learning without a fixed neighbor count. Evaluated on MIMIC‑IV and NIH‑TB datasets across five clinical prediction tasks, DIGOM achieves the highest AUROC while using fewer connections than existing multi‑interaction approaches, and shows robustness to missing modalities and task‑specific connectivity patterns.
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