Multimodal graph neural networks in healthcare: a review of fusion strategies ac...

연구 요약

Multimodal graph neural networks in healthcare: a review of fusion strategies across biomedical domains.

Frontiers in artificial intelligence 학술지에 발표된 이 연구는 Vaida M, Huang Z 외 연구팀이 수행하였습니다.

이 연구는 'Multimodal graph neural networks in healthcare: a review of fusion strategies across biomedical domains.'에 대한 과학적 분석을 제공합니다.

핵심 내용

Graph Neural Networks (GNNs) have transformed multimodal healthcare data integration by capturing complex, non-Euclidean relationships across diverse sources such as electronic health records, medical imaging, genomic profiles, and clinical notes. This review synthesizes GNN applications in healthcare, highlighting their impact on clinical decision-making through multimodal integration, advanced fusion strategies, and attention mechanisms. Key applications include drug interaction and discovery, cancer detection and prognosis, clinical status prediction, infectious disease modeling, genomics, and the diagnosis of mental health and neurological disorders. Various GNN architectures demonstrate consistent applications in modeling both intra- and intermodal relationships. GNN architectures, such as Graph Convolutional Networks and Graph Attention Networks, are integrated with Convolutional Neural Networks (CNNs), transformer-based models, temporal encoders, and optimization algorithms to facilitate robust multimodal integration. Early, intermediate, late, and hybrid fusion strategies, enhanced by attention mechanisms like multi-head attention, enable dynamic prioritization of critical relationships, improving accuracy and interpretability. However, challenges remain, including data heterogeneity, computational demands, and the need for greater interpretability. Addressing these challenges presents opportunities to advance GNN adoption in medicine through scalable, transparent GNN models.

일반인을 위한 해석

구체적인 실천 사항은 담당 의사 또는 약사와 상담하시기 바랍니다.

실천 사항

• 현재 복용 중인 약물이나 영양제에 대해 궁금한 점이 있다면 담당 의사 또는 약사와 상담하시기 바랍니다
• 약물이나 영양제의 용법·용량을 임의로 변경하지 마세요
• 이상 반응이 나타나면 즉시 전문가에게 문의하세요

의사/약사의 전문적 판단을 대체하지 않습니다 (PMID: 41584223)

📄 [전문 보기 (Markdown)](fulltext/41584223-multimodal-graph-neural-networks-in-healthcare-a-review-of-f.md)