Mining Disease Association Relationships in Electronic Health Records: A Link Prediction Algorithm Based on Node Embbeding

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Presentation paper for the 2017 International Conference on Knowledge Management. This paper uses a link prediction algorithm based on Node Embedding to map disease nodes in order to discover disease associations more precisely and predict the risk of certain diseases.

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8 p.

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Xia, Lixin; Yu, Huangyingzi; Dong, QingXing & Cao, Gaohui October 25, 2017.

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This paper is part of the collection entitled: International Conference on Knowledge Management (ICKM) and was provided by the UNT College of Information to the UNT Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 106 times. More information about this paper can be viewed below.

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Presentation paper for the 2017 International Conference on Knowledge Management. This paper uses a link prediction algorithm based on Node Embedding to map disease nodes in order to discover disease associations more precisely and predict the risk of certain diseases.

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8 p.

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Abstract: In the past two decades, enormous amount of health data has been generated and stored with the wide
use of electronic health records in healthcare system. How to extract the relationships between diseases
from these data for the accurate diagnosis and prediction of diseases has become a hot issue in both
academic and industry. However, many studies only considered several specific diseases instead of
revealing relationships among various diseases. Thus, in order to model associations between various
diseases, in this paper, a link prediction algorithm based on Node Embedding was put forward to map
the disease nodes to a n-dimension space and use neural network to learn node and network features
and obtain the vector representation of the nodes used as computing connection probability so as to
explode the knowledge of disease relationships. Compared with traditional algorithms of link
prediction, our results show that the proposed algorithm can capture the global information of the
disease network so as to discover disease associations more precisely and to predict the risk of certain
disease in the future more effectively.

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  • 13th International Conference on Knowledge Management, October 25-26, 2017. Dallas, Texas.

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International Conference on Knowledge Management (ICKM)

Serving as digital proceedings, this collection includes papers, posters, and slides from invited talks as well as practitioner and sponsor presentations for the annual International Conference on Knowledge Management (ICKM).

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  • October 25, 2017

Added to The UNT Digital Library

  • Oct. 26, 2017, 3:36 p.m.

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  • June 15, 2021, 5:41 p.m.

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Xia, Lixin; Yu, Huangyingzi; Dong, QingXing & Cao, Gaohui. Mining Disease Association Relationships in Electronic Health Records: A Link Prediction Algorithm Based on Node Embbeding, paper, October 25, 2017; (https://digital.library.unt.edu/ark:/67531/metadc1036588/: accessed January 17, 2025), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; crediting UNT College of Information.

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