关键词: 过程挖掘, 词对隐狄利克雷分布模型, 概率后缀树, 临床路径

Abstract: Standard clinical pathways play an important role in standardizing the treatment process and improving the therapeutic effect.However,current clinical pathways are designed for all patients of a disease,which cannot reflect the personalized information of patients or hospitals.To realize the personalized clinical pathways that meet the characteristics of patients and hospitals,the clinical pathway of drug treatment from prescription data recorded in medical information systems was mined.The prescription data and DrugBank database were used to generate the daily drug efficacy documents of patients.The token-bigram Latent Dirichlet Allocation (LDA) model was used to cluster these documents.The therapeutic topics were obtained at the same time.Finally,the probabilistic suffix tree model was trained as the clinical path of drug treatment by taking the patients' topic sequence of drug efficacy as input.The model could assist experts developing personalized clinical pathways and could be used for recommending follow-up medication as well.By taking the prescription data of pneumonia patients in MIMIC-Ⅲ database as an example,the feasibility and validity of the proposed method was verified.

Key words: process mining, token-bigram latent Dirichlet allocation model, probabilistic suffix tree, clinical path