ALIVE: A Multi-relational Link Prediction Environment for the Healthcare Domain
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.
KeywordsLink Prediction healthcare analytics multi-relational networks
Unable to display preview. Download preview PDF.
- 1.Al Hasan, M., Chaoji, V., Salem, S., Zaki, M.: Link prediction using supervised learning. In: Workshop on Link Discovery: Issues, Approaches and Apps, Citeseer (2005)Google Scholar
- 4.Davis, D., Lichtenwalter, R., Chawla, N.V.: Multi-relational link prediction in heterogeneous information networks. In: 2011 International Conference on Advances in Social Networks Analysis and Mining, pp. 281–288. IEEE (2011)Google Scholar
- 5.Davis, D.A., Chawla, N.V.: Exploring and exploiting disease interactions from multi-relational gene and phenotype networks. PloS One 6(7), e22670 (2011)Google Scholar
- 11.Leroy, V., Cambazoglu, B.B., Bonchi, F.: Cold start link prediction. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 393–402. ACM (2010)Google Scholar
- 13.Lichtenwalter, R.N., Lussier, J.T., Chawla, N.V.: New perspectives and methods in link prediction. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 243–252. ACM (2010)Google Scholar
- 15.Wang, C., Satuluri, V., Parthasarathy, S.: Local probabilistic models for link prediction. In: Seventh IEEE International Conference on Data Mining, ICDM 2007, pp. 322–331. IEEE (2007)Google Scholar