图谱人工智能在医学中的应用。

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Ruth Johnson, Michelle M Li, Ayush Noori, Owen Queen, Marinka Zitnik
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引用次数: 0

摘要

在临床人工智能(AI)领域,主要通过图神经网络和图转换器架构进行的图表示学习,因其能够捕捉临床数据集中错综复杂的关系和结构而脱颖而出。对于从病人记录到成像的各种数据,图人工智能模型通过将模式和其中的实体视为由其关系相互连接的节点,从而全面地处理数据。图谱人工智能促进了模型在临床任务中的转移,使模型能够在患者群体中推广,而无需额外参数,并且只需极少甚至无需重新训练。然而,在临床决策中,以人为本的设计和模型可解释性的重要性怎么强调都不为过。由于图人工智能模型是通过定义在关系数据集上的局部神经变换来捕捉信息的,因此在阐明模型原理方面既是机遇也是挑战。知识图谱可以将模型驱动的见解与医学知识相结合,从而提高可解释性。新兴的图人工智能模型通过预训练整合了多种数据模式,促进了交互式反馈循环,并促进了人类与人工智能的合作,为实现有临床意义的预测铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Graph Artificial Intelligence in Medicine.

In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical datasets. With diverse data-from patient records to imaging-graph AI models process data holistically by viewing modalities and entities within them as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters and with minimal to no retraining. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on relational datasets, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph AI models integrate diverse data modalities through pretraining, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way toward clinically meaningful predictions.

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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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