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为规范医疗行为、提高医疗质量并降低医疗成本,临床路径作为一种既能贯彻关键质量管理原则又能节约资源的标准化治疗模式,已被世界许多医院所采用。本文提出了一种基于案例推理的人工智能方法和Mulit-agent技术相结合的自适应临床路径建模的方法,给出了系统的总体框架和工作流程,以及各关键技术的实现方法。以某医院实施临床路径的日志文件和电子病例系统中的历史病例进行分类处理作为CBR的初始案例库,将未存入案例库的多个未执行的临床路径作为新案例,利用上述的CBR和Multi-agent的自适应系统进行临床路径的求解。测试运行结果检验了系统的可行性和有效性,对于在诊疗过程中如何确定灵活、自适应的临床路径有着重要和现实的指导意义。
In order to standardize the medical behaviors, improve the medical quality and reduce the medical costs, the clinical pathway has been adopted by many hospitals in the world as a standardized treatment model that can implement key quality management principles and save resources. In this paper, a method of adaptive clinical path modeling based on case-based artificial intelligence and Mulit-agent technology is proposed. The overall framework and workflow of the system and the implementation of each key technology are given. Using a log file of a clinical path in a hospital and historical cases in the electronic case system as the initial case base of CBR, a number of unexecuted clinical pathways that are not stored in the case base are used as a new case, and the above CBR and Multi-agent adaptive system for clinical path solution. Test results verify the feasibility and effectiveness of the system and provide important and practical guidance on how to determine a flexible and adaptive clinical path in the course of diagnosis and treatment.