  Background and Objective High medicine diversity has always been a
significant challenge for prescription, causing confusion or doubt in
physicians' decision-making process. This paper aims to develop a medicine
recommender system called RecoMed to aid the physician in the prescription
process of hypertension by providing information about what medications have
been prescribed by other doctors and figuring out what other medicines can be
recommended in addition to the one in question. Methods There are two steps to
the developed method: First, association rule mining algorithms are employed to
find medicine association rules. The second step entails graph mining and
clustering to present an enriched recommendation via ATC code, which itself
comprises several steps. First, the initial graph is constructed from
historical prescription data. Then, data pruning is performed in the second
step, after which the medicines with a high repetition rate are removed at the
discretion of a general medical practitioner. Next, the medicines are matched
to a well-known medicine classification system called the ATC code to provide
an enriched recommendation. And finally, the DBSCAN and Louvain algorithms
cluster medicines in the final step. Results A list of recommended medicines is
provided as the system's output, and physicians can choose one or more of the
medicines based on the patient's clinical symptoms. Only the medicines of class
2, related to high blood pressure medications, are used to assess the system's
performance. The results obtained from this system have been reviewed and
confirmed by an expert in this field.
