ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2020, Vol. 57 ›› Issue (8): 1741-1754.doi: 10.7544/issn1000-1239.2020.20200149

所属专题: 2020数据挖掘与知识发现专题

• 人工智能 • 上一篇    下一篇

基于多源情境协同感知的药品推荐

郑值1,徐童1,秦川1,廖祥文2,郑毅3,刘同柱4,童贵显4   

  1. 1(中国科学技术大学计算机学院 合肥 230027);2(福州大学数学与计算机科学学院 福州 350116);3(华为技术有限公司 杭州 310051);4(中国科学技术大学附属第一医院 合肥 230027) (zhengzhi97@mail.ustc.edu.cn)
  • 出版日期: 2020-08-01
  • 基金资助: 
    国家重点研发计划项目(2018YFB1004300);国家自然科学基金项目(U1605251,61703386);中央高校基本科研业务费专项资金(WK9110000014);安徽省重点研发计划项目(1804b06020377)

Multi-Source Contextual Collaborative Recommendation for Medicine

Zheng Zhi1, Xu Tong1, Qin Chuan1, Liao Xiangwen2, Zheng Yi3, Liu Tongzhu4, Tong Guixian4   

  1. 1(School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027);2(College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116);3(Huawei Technologies., Hangzhou 310051);4(The First Affiliated Hospital of the University of Science and Technology of China, Hefei 230027)
  • Online: 2020-08-01
  • Supported by: 
    This work was supported by the National Key Research and Development Program of China (2018YFB1004300), the National Natural Science Foundation of China (U1605251, 61703386), the Fundamental Research Funds for the Central Universities (WK9110000014), and the Key Research and Development Program of Anhui Province (1804b06020377).

摘要: 电子医疗记录的快速积累与数据分析技术的日益成熟,为实现包含智能诊断与药品推荐等功能的智慧医疗服务奠定了基础.然而,电子病历的精简性与患者症状描述的模糊性,导致诊断模型容易受到高发疾病与常见症状的干扰,从而无法支撑细粒度的诊断与处方,在药品推荐上缺乏针对性.与此同时,病情描述以外的许多情境信息,如患者的性别、年龄等个人信息,诊疗过程、检查结果等记录信息,以及所在地的天气、温差等外部信息等,也对于细化对于患者的诊断和处方有着重要的辅助作用.然而,这些多源异构信息往往难以被现有技术所有效提取与整合,从而限制了病情诊断与药品推荐的有效性.针对这一问题,提出了一种基于多源情境协同感知的药品推荐方法,在有效整合多源异构情境信息的基础上,为实现病情诊断与药品推荐提供了具有可解释性的依据.具体而言,首先使用词袋模型对病历和相应的情境数据进行处理,然后设计了一种基于LDA模型的情境主题模型Medicine-LDA,在融合患者病情描述与相应情境信息的同时,有效缓解了情境信息组合爆炸的问题.基于某大型三甲医院的电子病历数据集上的对比实验证明了该方法的有效性与鲁棒性.

关键词: 情境感知, 药品推荐, 主题模型, 多标签学习, 推荐系统

Abstract: Recent years have witnessed the accumulation of electronic medical records (EMR), as well as the rapid development of data analytics techniques, which highly support the intelligent medical services, e.g., automatic diagnosis and medicine recommendation. Unfortunately, due to the simplicity of general EMR, the diagnosis model could be easily disturbed by common diseases or symptoms, thus fine-grained prescription with personalized focalization will hardly be achieved. At the same time, we realize that some related context information, e.g., personalized information like age and sexuality, treatment records like examinations, and external information like weather and temperature, could all benefit the diagnosis and medicine recommendation task. However, these information could not be effectively extracted and integrated by current techniques, which constrains the performance of medicine recommendation. To that end, in this paper, we propose a comprehensive framework based on the collaborative awareness of multi-source context information. Specifically, we first utilize the bag-of-words model to process the EMR and related context records. Along this line, a LDA-based contextual collaborative model called Medicine-LDA has been designed to integrate the multi-source information, while at the same time, alleviate the problem of combination explosion of context information. Extensive experiments on the real-world data set from a first-rate hospital demonstrate the effectiveness of our solution.

Key words: context-aware, medicine recommendation, topic model, multi-label learning, recommender system

中图分类号: