RecoMed: A knowledge-aware recommender system for hypertension medications

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. 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. 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. Unlike other research studies, in this work, the framework proposed is entirely based on unsupervised learning methods and so it is more likely to be used in real-world applications as medical data are generally unlabeled, and supervised learning models cannot be directly built using these types of data. The results obtained from this system have been reviewed and confirmed by an expert in this field.

Abstract licence: CC BY-NC-ND

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