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Analysis on Correlation between Prescriptions and Test Results of Diabetes Patients using Graph Models and Node Centrality
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 Title & Authors
Analysis on Correlation between Prescriptions and Test Results of Diabetes Patients using Graph Models and Node Centrality
Yoo, Kang Min; Park, Sungchan; Rhee, Su-jin; Yu, Kyung-Sang; Lee, Sang-goo;
This paper presents the results and the process of extracting correlations between events of prescriptions and examinations using graph-modeling and node centrality measures on a medical dataset of 11,938 patients with diabetes mellitus. As the data is stored in relational form, RDB2Graph framework was used to construct effective graph models from the data. Personalized PageRank was applied to analyze correlation between prescriptions and examinations of the patients. Two graph models were constructed: one that models medical events by each patient and another that considers the time gap between medical events. The results of the correlation analysis confirm current medical knowledge. The paper demonstrates some of the note-worthy findings to show the effectiveness of the method used in the current analysis.
medical data;graph theory;graph modeling;PageRank;data mining;correlation analysis;
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