Abstract

Info

Title:: Hypertension related association mining based on co-word analysis and visualization

Author(s):: LIU Li1; YAO Jingjing1; LI Jun2; CHEN Xianlai3; ZHOU Yukui1; 1. School of Life Science, Central South University, Changsha 410013, China; 2. Xiangya School of Stomatology, Central South University, Changsha 410008, China; 3. Institute of Information Security and Big Data, Central South University, Changsha 410083, China

Keywords:: ; Keywords: electronic medical record; medical record home page; hypertension; co-word analysis; visualization

Abstract:: Abstract: Objective To analyze and mine the electronic medical record home page of patients with hypertension, and to reveal the relationships among disease diagnoses. Methods Based on the co-word analysis, the analysis module was established by Python language, and the analysis results were displayed by Gephi complex network analysis software. Results Based on 3 362 electronic medical records, a disease diagnosis co-occurrence network containing 1 029 disease diagnosis nodes and 12 479 co-occurrence relationship lines was constructed, and a disease diagnosis cluster with strong co-occurrence relationship was found. Conclusion The disease diagnosis co-occurrence network can be interpreted from multi-angle and multi-level, and can be displayed in a visual map to reveal the relationships among disease diagnoses, laying a foundation for the further establishment of a more complete disease map.