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.
翻译:高医疗背景和目标:高医学多样性一直是处方的一大挑战,在医生的决策过程中造成了混乱或怀疑。本文件旨在开发一个名为RecoMed的医学建议系统,帮助医生进行高血压处方过程,提供有关其他医生处方药物的信息,并找出除了有关药物之外还可以建议哪些其他药物。方法上有两个步骤,即:第一,采用联合规则采矿算法来寻找医学协会规则。第二步是图解采矿和分组,通过ATC代码提出丰富建议,该代码本身包括若干步骤。第一,初步图表是根据历史处方数据构建的。然后,在第二步进行数据处理,此后,由普通医生酌情决定取消高重复率的药物。第二步,药物与著名的医学分类系统(ATC代码用来提供丰富建议)相匹配。最后一步是DBSCAN和Louvain算法组药物。最后一步是提供建议药物清单,作为系统输出数据,最初的图表来自历史处方数据。然后,医生们可以在第二步中选择一种或更多一种高血压的药物。根据这个系统进行临床诊断结果评估。