ALIVE: A Multi-relational Link Prediction Environment for the Healthcare Domain

  • Reid A. Johnson
  • Yang Yang
  • Everaldo Aguiar
  • Andrew Rider
  • Nitesh V. Chawla
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7769)


An underlying assumption of biomedical informatics is that decisions can be more informed when professionals are assisted by analytical systems. For this purpose, we propose ALIVE, a multi-relational link prediction and visualization environment for the healthcare domain. ALIVE combines novel link prediction methods with a simple user interface and intuitive visualization of data to enhance the decision-making process for healthcare professionals. It also includes a novel link prediction algorithm, MRPF, which outperforms many comparable algorithms on multiple networks in the biomedical domain. ALIVE is one of the first attempts to provide an analytical and visual framework for healthcare analytics, promoting collaboration and sharing of data through ease of use and potential extensibility. We encourage the development of similar tools, which can assist in facilitating successful sharing, collaboration, and a vibrant online community.


Link Prediction healthcare analytics multi-relational networks 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Reid A. Johnson
    • 1
  • Yang Yang
    • 1
  • Everaldo Aguiar
    • 1
  • Andrew Rider
    • 1
  • Nitesh V. Chawla
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of Notre DameNotre DameUSA

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