中文摘要：

癫痫是一种较为常见的脑部疾病,也是一种长期性神经系统疾患。目前全球有大量的患者饱受癫痫带来的痛苦。癫痫虽然不能根治,但是约70%癫痫病例的抽搐发作可以通过药物控制。电子健康病历蕴含着众多癫痫患者的信息,为个性化药物的处方提供了海量大数据资源。文章通过对医疗电子病例进行大数据分析,提出一种基于隐性反馈模型与交叉推荐的药物推荐方法（Implicit Feedback and Crossing Recommendation,IFCR）,以帮助医生选择合适的药物。该方法通过分析患者的看病历史以及相似患者的看病经历,建立患者症状与医生用药之间的对应关系,从而根据患者症状为医生提供药物推荐。与基于人工神经网络的药物推荐系统进行对比试验发现,文章提出的药物推荐系统在召回率上具有显著优势,而在精确率上二者各有优劣。总体来看,文章所提出的IFCR方法效果更为出色。最后,通过对两种方法的推荐结果做进一步分析发现,二者推荐倾向不同,因此存在建立集成模型的可能。

英文摘要：

Epilepsy refers to a set of chronic neurological syndromes characterized by transient and unexpected electrical disturbances of the brain. There are a large number of patients suffering from epilepsy in the world. Epilepsy cannot be cured eventually, but 70% of seizures can be kept under control by drugs. Electronic health records（EHRs） of epileptics contain a wealth of information for personalized medicine prescription, providing a large number of data resources. Based on real medical electronic cases for large data analysis, this paper proposes a drug recommendation system based on implicit feedback and crossing recommendation（IFCR） to help doctors choose right drugs. The proposed system aims to analyze the patients＇ medical history and similar patients＇ in order to find the relationships between syndromes and drugs. Comparing our system with the one based on artificial neural network（ANN）, the proposed algorithm performs much better than ANN in terms of the recall rate with a 30% improvement. However, two algorithms have different performance on the precision rate. In general, the performance of IFCR is better than that of ANN. Finally, we analyze the recommendation results of two algorithms and discover it is possible to propose an ensemble model to compile IFCR with ANN.