基于CBR和Multi-agent的自适应临床路径系统

Self-adaptive Clinical Pathway System Based on CBR and Multi-agent

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。

To regulate medical behavior, improve medical quality and reduce health-care costs, clinical pathway has been adopted by many hospitals around the world as a standardized treatment model for both implementing the key principles of quality management and saving resources. This paper proposes an artificial intelligence method of case-based reasoning （CBR） and multi-agent technology, which completes the prototype decision support system of case-based reasoning self-adaptive clinical pathway. The overall framework and procedures of the system are given, as well as key technologies of the implementation methods. The log files in the implementation of clinical pathway in some hospitals and the historical eases of electronic medical record system are classified as an initial ease base of CBR. Meanwhile, multiple unimplemented clinical pathways which are not deposited into the case base are taken as new cases. Then the clinical pathway was solved by using the above self-adaptive system based on CBR. The results of operating examples examined the feasibility and effeetiveness of the system, thus providing important and realistic guidance on how to determine flexible, self-adaptive clinical pathway in the treatment process.