Rapid increases in data volume and variety pose a challenge to safe drug prescription for health professionals like doctors and dentists. This is addressed by our study, which presents innovative approaches in mining data from drug corpus and extracting feature vectors to combine this knowledge with individual patient medical profiles. Within our three-tiered framework—the prediction layer, the knowledge layer and the presentation layer—we describe multiple approaches in computing similarity ratios from the feature vectors, illustrated with an example of applying the framework in a typical medical clinic. Experimental evaluation shows that the word embedding model performs better than the adverse network model, with a F score of 0.75. The F score is a common metrics used for evaluating the performance of classification algorithms. Similarity to a drug the patient is allergic to or is taking are important considerations for the suitability of a drug for prescription. Hence, such an approach, when integrated within the clinical work-flow, will reduce prescription errors thereby increasing patient health outcomes.

数据量和种类的快速增加对医生和牙医等卫生专业人员的安全药物处方提出了挑战。我们的研究解决了这一问题，该研究提出了从药物语料库中挖掘数据和提取特征向量以将这些知识与个体患者医疗档案相结合的创新方法。在我们的三层框架（预测层、知识层和表示层）中，我们描述了从特征向量计算相似度的多种方法，并以在典型医疗诊所中应用该框架的示例进行说明。实验评估表明，词嵌入模型的表现优于逆向网络模型，F值为 0.75。F _分数是用于评估分类算法性能的常用指标。与患者过敏或正在服用的药物的相似性是药物是否适合处方的重要考虑因素。因此，当这种方法整合到临床工作流程中时，将减少处方错误，从而提高患者的健康结果。