Graph data base: an enabling technology for drug prescription patterns analysis

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Ilaria Giordani
Francesco Archetti
Antonio Candelieri
Gaia Arosio
Roberto Mattina


This paper has two main objectives: first to show that new data base technologies (DB) like graph data bases can enable the efficient design and implementation of network -based models, second that this type of models enables new insights on biomedical data and in particular prescription patterns allowing to link data about patients, prescriptions and prescriber. Albeit the application domain is potentially the whole field of health care data, the focus of this paper is on prescription patterns and specifically of antibiotics whose prescription pattern is difficult to analyze due to the antibiotics resistance. This problem can take advantage of the approach proposed: a network-based model, specifically suitable for community-based medicine, which is a suitable framework for antibiotics prescription and resistance analysis.

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