Finding Relations Between Diseases by Age-Series Based Supervised Link Prediction - Kaya, Buket; Poyraz, Mustafa (Assoc Computing Machinery, 2015)
In the last few years we are witnessing to an increasing interest in the application of social network analysis and methods to health care information and management systems. The utilization of information technologies that could significantly improve efficiency and effectiveness of health care strategies are very important because of the implications they could have in everyday life of individuals. In this paper, we predict the onset of future diseases on the base of the current health status of patients. The problem of predicting the relations between diseases is a really difficult and, at the same time, an important task. For this purpose, this paper first constructs a weighted disease network and then, it proposes a novel link prediction method with supervised strategy to identify the connections between diseases, building the evolving structure of the disease network with respect to patients' ages. Experiments on a real network demonstrate that the proposed approach can reveal disease correlations accurately and perform well at capturing future disease risks.
IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) -- AUG 25-28, 2015 -- Paris, FRANCE
Keywords: link prediction; health informatics; disease networks; supervised learning