Multi-omics Graph Knowledge Representation for Pneumonia Prognostic Prediction.

IF 6.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-10-30 DOI:10.1109/JBHI.2024.3488735
Wenyu Xing, Miao Li, Yiwen Liu, Xin Liu, Yifang Li, Yanping Yang, Jing Bi, Jiangang Chen, Dongni Hou, Yuanlin Song, Dean Ta
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Abstract

Early prognostic prediction is crucial for determining appropriate clinical interventions. Previous single-omics models had limitations, such as high contingency and overlooking complex physical conditions. In this paper, we introduced multi-omics graph knowledge representation to predict in-hospital outcomes for pneumonia patients. This method utilizes CT imaging and three non-imaging omics information, and explores a knowledge graph for modeling multi-omics relations to enhance the overall information representation. For imaging omics, a multichannel pyramidal recursive MLP and Longformer-based 3D deep learning module was developed to extract depth features in lung window, while radiomics features were simultaneously extracted in both lung and mediastinal windows. Non-imaging omics involved the adoption of laboratory, microbial, and clinical indices to complement the patient's physical condition. Following feature screening, the similarity fusion network and graph convolutional network (GCN) were employed to determine omics similarity and provide prognostic prediction. The results of comparative experiments and generalization validation demonstrat that the proposed multi-omics GCN-based prediction model has good robustness and outperformed previous single-type omics, classical machine learning, and previous deep learning methods. Thus, the proposed multi-omics graph knowledge representation model enhances early prognostic prediction performance in pneumonia, facilitating a comprehensive assessment of disease severity and timely intervention for high-risk patients.

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用于肺炎预后预测的多组学图知识表示法
早期预后预测对于确定适当的临床干预措施至关重要。以往的单一组学模型有其局限性,如或然率高、忽略复杂的物理条件等。在本文中,我们引入了多组学图知识表示法来预测肺炎患者的院内预后。该方法利用 CT 成像和三种非成像全息信息,并探索了一种多组学关系建模的知识图谱,以增强整体信息表示。在成像组学方面,开发了多通道金字塔递归MLP和基于Longformer的三维深度学习模块,以提取肺窗的深度特征,同时提取肺窗和纵隔窗的放射组学特征。非成像组学包括采用实验室、微生物和临床指数来补充患者的身体状况。在特征筛选之后,采用相似性融合网络和图卷积网络(GCN)来确定全息图学相似性并提供预后预测。对比实验和泛化验证的结果表明,所提出的基于多组学 GCN 的预测模型具有良好的鲁棒性,优于以往的单一类型组学、经典机器学习和以往的深度学习方法。因此,所提出的多组学图知识表示模型提高了肺炎的早期预后预测性能,有助于全面评估疾病的严重程度并对高危患者进行及时干预。
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来源期刊
IEEE Journal of Biomedical and Health Informatics
IEEE Journal of Biomedical and Health Informatics COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
13.60
自引率
6.50%
发文量
1151
期刊介绍: IEEE Journal of Biomedical and Health Informatics publishes original papers presenting recent advances where information and communication technologies intersect with health, healthcare, life sciences, and biomedicine. Topics include acquisition, transmission, storage, retrieval, management, and analysis of biomedical and health information. The journal covers applications of information technologies in healthcare, patient monitoring, preventive care, early disease diagnosis, therapy discovery, and personalized treatment protocols. It explores electronic medical and health records, clinical information systems, decision support systems, medical and biological imaging informatics, wearable systems, body area/sensor networks, and more. Integration-related topics like interoperability, evidence-based medicine, and secure patient data are also addressed.
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