Age-series based link prediction in evolving disease networks

• A weighted disease network which indicates the relationships between diseases.
• A novel age-series based link prediction method for the disease network.
• A large number of correlations are hinted by the topology of a disease network.
• Medical histories of a set of similar patients are important for prediction.
• Good performance values for the case-based link prediction.

Recently, several research efforts based on social network analysis and methods have been made for medical care information. One of these efforts is to extract the relationships between diseases by using social network modeling. However, all of previous works used the relationships in a simple way in a network consisting of diseases regardless of time or age factors. In this paper, we predict the onset of future diseases on the basis of the current health status of patients by considering age factor. 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, to identify the connections between diseases, building the evolving structure of the disease network with respect to patients’ ages. To the best of our knowledge, this is the first attempt in predicting the connections between diseases according 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.