RecoMed: A knowledge-aware recommender system for hypertension medications

نویسندگان

چکیده

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 recommender system called RecoMed aid the physician prescription process of hypertension by providing information about what medications have prescribed other doctors and figuring out medicines can be recommended addition one question. There are two steps developed method: First, association rule mining algorithms employed find rules. The second step entails graph clustering present an enriched recommendation via ATC code, which itself comprises several steps. initial is constructed from historical data. Then, data pruning performed step, after with high repetition rate removed at discretion general medical practitioner. Next, matched well-known classification code provide recommendation. And finally, DBSCAN Louvain cluster final step. A list provided as system's output, physicians choose more based on patient's clinical symptoms. Only class #2, related blood pressure medications, used assess performance. Unlike research studies, this work, framework proposed entirely unsupervised learning methods so it likely real-world applications generally unlabeled, supervised models cannot directly built using these types results obtained reviewed confirmed expert field.

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ژورنال

عنوان ژورنال: Informatics in Medicine Unlocked

سال: 2022

ISSN: ['2352-9148']

DOI: https://doi.org/10.1016/j.imu.2022.100950