Unsupervised link prediction in evolving abnormal medical parameter networks

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. The prediction of abnormal medical parameters based on the past patient medical history revealed efficacious in foreseeing medical signs a patient could likely be affected in the future. In this paper, we predict the onset of future signs on the base of the current health status of patients. For this purpose, we first construct a weighted abnormal medical parameter network considering the relations between abnormal parameters. Then, we propose an unsupervised link prediction method to identify the connections between abnormal parameters, building the evolving structure of abnormal parameter 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 signs.