标题：Drug recommendation method based on medical process mining and patient signs [基于医疗过程挖掘与患者体征的药物推荐方法]
作者：Li, Pengfei ;Lu, Faming ;Bao, Yunxia ;Zeng, Qingtian ;Zhu, Guanye
作者机构：[Li, Pengfei ;Lu, Faming ;Bao, Yunxia ;Zhu, Guanye ] College of Computer Science and Engineering, Shandong University of Science and Technology, Qingd 更多
通讯作者地址：[Lu, F] College of Computer Science and Engineering, Shandong University of Science and TechnologyChina;
来源：Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS
关键词：Latent Dirichlet allocation topic model; Probabilistic suffix tree; Process mining; Process model; XGBoost algorithm
摘要：Mining medical data to generate a standard treatment process model of a disease, or to provide decision support for treatment plan making is one of the research hotspots. Based on the drug data during hospitalization, by analyzing the drug treatment process model of the diseases, a new drug recommendation method involving the data of medication and signs of current patients was proposed. Specifically, for a given disease, a daily medication list was generated based on historical patient prescribing data. Latent Dirichlet Allocation(LDA) topic model was used to train the medication data of all patients, so as to obtain the efficacy topic of drug therapy and the distribution of efficacy topic of each treatment day. Moreover, with the topic distribution of patients' efficacy of each day clustered, the patients' drug treatment process was transformed into drug efficacy combination label sequence, on which the probability suffix tree model of drug treatment process was built. The probability distribution of future efficacy predicted by probability suffix tree was computed and was taken as features with patient's sign vector, the combination of efficacy corresponding to the actual medication of patients were seen as classification labels. The XGBoost was used to train the classification model and make patient drug recommendation. The feasibility and validity of the proposed method were evaluated by using the prescription log and characteristic data of patients with diabetes in MIMIC-Ⅲ database. © 2020, Editorial Department of CIMS. All right reserved.