具有自学习功能的合理用药系统设计与应用研究

发布时间：2018-10-23 12:25

【摘要】：伴随着医疗卫生事业及医药科技的快速发展,临床医疗活动中的药物使用问题也逐步成为公众关注的焦点。而随着药物的广泛使用,药源性疾病对人类健康的危害也越来越大,合理使用药物是降低药物不良反应危害的重要前提。合理用药需要根据疾病类型、病人情况和药理学理论选择最佳的药物及其制剂,制定或调整给药方案,以及有效、安全、经济地防治和治愈疾病的措施。本文主要针对合理用药问题,着重对系统的标准化诊断数据库和过程记录数据库进行分析研究,通过采用基于实例学习方法及粗糙集理论相关算法,最终使合理用药系统具有自学习的功能,促进临床合理用药。粗糙集理论是一种用来处理不确定、不一致和不精确数据的数学理论。在本课题中,使用相应的粗糙集算法,计算出决策属性对条件属性的依赖程度,确定了各特征属性的权重值。另一方面,使用基于正区域的约简算法来处理药品组合与治疗效果数据表,将处理后所得的知识规则作为专家组制定新处方用药方案的依据。本文是将基于实例学习与粗糙集属性权值化算法进行融合,实现病例检索,使医生可将系统提供的医嘱方案作为给药的参考。针对药品组合与治疗效果数据表,采用粗糙集约简算法,得到对治疗起关键作用的药品组合,该药品组合为专家组提供了有效的支持。如上设计流程不但使系统实现自学习,而且使合理用药系统学习效率得到了提高。而本课题的研究成果,将对合理用药的发展起着重要的作用,同时为临床医生给患者在治疗过程中药物治疗方案的制定提供了支持。
[Abstract]:With the rapid development of medical and medical science and technology, the problem of drug use in clinical medical activities has gradually become the focus of public attention. With the wide use of drugs, drug-induced diseases are more and more harmful to human health. Rational use of drugs is an important prerequisite to reduce adverse drug reactions. Rational drug use needs to choose the best drug and its preparation according to the disease type, patient condition and pharmacology theory, to formulate or adjust the drug administration plan, and to prevent and cure the disease effectively, safely and economically. Aiming at the problem of rational use of drugs, this paper focuses on the analysis and research of the standardized diagnosis database and process record database of the system, and adopts the case-based learning method and rough set theory related algorithms. Finally, the rational drug use system has the function of self-learning and promotes the rational use of drugs in clinic. Rough set theory is a mathematical theory used to deal with uncertain, inconsistent and imprecise data. In this paper, the corresponding rough set algorithm is used to calculate the degree of dependence of decision attributes on conditional attributes, and the weights of each feature attribute are determined. On the other hand, a reduction algorithm based on positive region is used to deal with the data table of drug combination and therapeutic effect, and the rules of knowledge obtained from the treatment are taken as the basis for the expert group to formulate a new prescription drug use scheme. In this paper, case-based learning and rough set attribute weighted algorithm are fused to realize case retrieval, so that doctors can use the medical advice scheme provided by the system as the reference for drug delivery. According to the data table of drug combination and therapeutic effect, rough set reduction algorithm is used to obtain the drug combination which plays a key role in the treatment. The combination provides effective support for the expert group. The above design process not only makes the system realize self-learning, but also improves the learning efficiency of rational drug use system. The research results of this topic will play an important role in the development of rational drug use, and provide support for the clinicians to make the drug treatment plan.
【学位授予单位】：大连海事大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TP311.13;R318.6

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