Link Prediction in Weighted Symptom Networks

Abstract

The saying treat the disease, not the symptoms is widespread, a cliche for eliminating or repairing the root of a problem rather than mitigating the negative effects. It is taken for granted that prevention is the best course of action. It is ironic, then, that many of today's best disease treatments are actually symptom suppressors. This paper predicts the onset of future symptoms on the base of the current health status of patients. The problem of predicting the relations between symptoms (abnormal parameters in this paper) which can be shown as the reason of a disease in the future is a really difficult and, at the same time, an important task. For this purpose, the present paper first constructs a weighted symptom networks considering the relations between abnormal parameters. Then, it proposes a link prediction method to identify the connections between parameters, building the evolving structure of symptom network with respect to patients' ages. To the best of our knowledge, this is the first attempt in predicting the connections between the results of laboratory tests. Experiments on a real network demonstrate that the proposed approach can reveal new abnormal parameter correlations accurately and perform well at capturing future disease risks.