医学专家系统知识挖掘的高阶规划方法
Higher Order Programming to Mine Knowledge for a Modern Medical Expert System
摘要：
知识挖掘是从大量的数据和背景知识中获取新的有用知识的过程。现代医疗机构定期生成大量存储在数据库中的电子数据。这些数据是一个宝贵的资源，挖掘有用的知识，帮助医生作出适当和准确的诊断和治疗疾病的决定。在本文中，我们提出了一个新的医疗专家系统的设计基于逻辑编程框架。建议的系统包括一个知识挖掘组件作为一个剧目的工具，发现有用的知识。基于高阶和元级编程方案，使用Prolog实现了分类和关联挖掘工具，以体现基于逻辑的语言的强大功能。这种语言还提供了模式匹配功能，这是开发知识密集型任务的基本功能。除了医疗决策支持的主要目标外，我们基于逻辑的知识挖掘组件发现的知识还可以作为背景知识部署到来自其他来源的预处理数据，以及保护数据存储库免受约束违反。还提出了一个知识部署框架。
英文摘要：
Knowledge mining is the process of deriving new and useful knowledge from vast volumes of data and background knowledge. Modern healthcare organizations regularly generate huge amount of electronic data stored in the databases. These data are a valuable resource for mining useful knowledge to help medical practitioners making appropriate and accurate decision on the diagnosis and treatment of diseases. In this paper, we propose the design of a novel medical expert system based on a logic-programming framework. The proposed system includes a knowledge-mining component as a repertoire of tools for discovering useful knowledge. The implementation of classification and association mining tools based on the higher order and meta-level programming schemes using Prolog has been presented to express the power of logic-based language. Such language also provides a pattern matching facility, which is an essential function for the development of knowledge-intensive tasks. Besides the major goal of medical decision support, the knowledge discovered by our logic-based knowledge-mining component can also be deployed as background knowledge to pre-treatment data from other sources as well as to guard the data repositories against constraint violation. A framework for knowledge deployment is also presented.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
