ALIVE: a multi-relational link prediction environment for the healthcare domain

Authors:
Reid A. Johnson;Yang Yang;Everaldo Aguiar;Andrew Rider;Nitesh V. Chawla
Affiliations:
Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN;Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN;Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN;Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN;Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN
Venue:
PAKDD'12 Proceedings of the 2012 Pacific-Asia conference on Emerging Trends in Knowledge Discovery and Data Mining
Year:
2012

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Abstract

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.