Recent Advances on Graph Analytics and Its Applications in Healthcare
Recent Advances on Graph Analytics and Its Applications in Healthcare
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DOI：
10.1145/3394486.3406469
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Fei Wang;Peng Cui;J. Pei;Yangqiu Song;Chengxi Zang
中科院分区：
文献类型：
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作者：
Fei Wang;Peng Cui;J. Pei;Yangqiu Song;Chengxi Zang
图是一种自然的表示方法，它编码了数据样本的特征和它们之间的关系。图分析是数据挖掘中的一个经典课题，过去已经提出了许多技术。近年来，随着数据挖掘和知识发现的迅速发展，许多新颖的图分析算法被提出并成功应用于各个领域。本教程的目标是总结最近开发的图形分析算法以及它们如何应用于医疗保健。特别是，我们的教程将涵盖技术进步和在医疗保健中的应用。在技术方面，我们将介绍深度网络嵌入技术，图神经网络，知识图构建和推理，图生成模型和图神经常微分方程模型。在医疗保健方面，我们将介绍如何将这些方法应用于临床风险的预测建模（例如，慢性病发作、住院死亡率、病情恶化等）以及具有多模态患者数据的疾病子分型（例如，电子健康记录、医学图像和多组学），从生物医学文献中发现知识并与数据驱动模型集成，以及药物研究和开发（例如，从头化合物设计和优化，临床试验招募和药物警戒的患者相似性）。我们将以一系列潜在的问题和挑战来结束整个教程，例如可解释性，公平性和安全性。特别是，考虑到COVID-19的全球大流行，我们还将总结现有的研究，这些研究已经利用图形分析来帮助理解COVID-19的机制，传播，治疗和预防，并指出未来研究的可用资源和潜在机会。
Graph is a natural representation encoding both the features of the data samples and relationships among them. Analysis with graphs is a classic topic in data mining and many techniques have been proposed in the past. In recent years, because of the rapid development of data mining and knowledge discovery, many novel graph analytics algorithms have been proposed and successfully applied in a variety of areas. The goal of this tutorial is to summarize the graph analytics algorithms developed recently and how they have been applied in healthcare. In particular, our tutorial will cover both the technical advances and the application in healthcare. On the technical aspect, we will introduce deep network embedding techniques, graph neural networks, knowledge graph construction and inference, graph generative models and graph neural ordinary differential equation models. On the healthcare side, we will introduce how these methods can be applied in predictive modeling of clinical risks (e.g., chronic disease onset, in-hospital mortality, condition exacerbation, etc.) and disease subtyping with multi-modal patient data (e.g., electronic health records, medical image and multi-omics), knowledge discovery from biomedical literature and integration with data-driven models, as well as pharmaceutical research and development (e.g., de-novo chemical compound design and optimization, patient similarity for clinical trial recruitment and pharmacovigilance). We will conclude the whole tutorial with a set of potential issues and challenges such as interpretability, fairness and security. In particular, considering the global pandemic of COVID-19, we will also summarize the existing research that have already leveraged graph analytics to help with the understanding the mechanism, transmission, treatment and prevention of COVID-19, as well as point out the available resources and potential opportunities for future research.