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. However, the effort to prevent the negative effects which may be reason of a disease is the best course of action. The prediction of symptoms based on the past patient medical history revealed efficacious in foreseeing symptoms (abnormal parameters) a patient could likely be affected in the future. In this paper, we predict the onset of future symptoms on the base of the current health status of patients. For this purpose, we first construct a weighted symptom network considering the relations between abnormal parameters. Then, we propose an unsupervised link prediction method to identify the connections between parameters, building the evolving structure of symptom network with respect to patients’ ages. 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.
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Kaya, B., Poyraz, M. (2017). Extracting Relations Between Symptoms by Age-Frame Based Link Prediction. In: Kawash, J., Agarwal, N., Özyer, T. (eds) Prediction and Inference from Social Networks and Social Media. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-51049-1_4
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DOI: https://doi.org/10.1007/978-3-319-51049-1_4
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