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New DIGOM framework enhances multimodal healthcare outcome prediction

By PulseAugur Editorial · [1 sources] ·

Researchers have developed DIGOM, a novel graph-based framework designed to improve outcome prediction in multimodal healthcare data. DIGOM explicitly models three types of interactions: within-patient cross-modal, cross-patient same-modal, and cross-patient cross-modal. The framework learns a sparse multiplex graph with disentangled views, enabling end-to-end learning and selecting informative relationships. Evaluated on MIMIC-IV and NIH-TB datasets across five clinical prediction tasks, DIGOM achieved the highest AUROC scores while demonstrating robustness to missing modalities. AI

IMPACT This framework could improve diagnostic accuracy and treatment planning in healthcare by better leveraging complex patient data.

RANK_REASON The cluster contains a research paper detailing a new model and its evaluation on healthcare datasets. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New DIGOM framework enhances multimodal healthcare outcome prediction

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The cluster contains a research paper detailing a new model and its evaluation on healthcare datasets. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Hakima Laribi, Olivier Lefebvre, Dan Poenaru, Martin Valli\`eres ·

    DIGOM: Disentangled Interaction Graphs for Outcome prediction in Multimodal healthcare

    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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