A self-learning expert system for diagnosis in traditional Chinese medicine

A novel self-learning expert system for diagnosis in Traditional Chinese medicine (TCM) was constructed by incorporating several data mining techniques, mainly including an improved hybrid Bayesian network learning algorithm, Naı̈ve–Bayes classifiers with a novel score-based strategy for feature selection and a method for mining constrained association rules. The data-driven nature distinguished the system from those existing TCM expert systems based on if-then rules to address knowledge elicitation problem. Moreover, the learned knowledge was provided in multiple forms including causal diagram, association rule and reasoning rules derived from classifiers. Finally, five representative cases were diagnosed to evaluate the performance of the system and the encouraging results were obtained. The results show that the prototype system performs well in diagnosis of TCM, and could be expected to be useful in the practice of TCM.     

National semantic infrastructure for traditional Chinese medicine

1 Introduction Traditional Chinese medicine (TCM) is the traditional medicine that originated in China,and is characterized by holism and treatment based     

Conceptual graph-based knowledge representation for supporting reasoning in African traditional medicine

Although African patients use both conventional or modern and traditional healthcare simultaneously, it has been proven that 80% of people rely on African traditional medicine (ATM). ATM includes medical activities stemming from practices, customs and traditions which were integral to the distinctive African cultures. It is based mainly on the oral transfer of knowledge, with the risk of losing critical knowledge. Moreover, practices differ according to the regions and the availability of medicinal plants. Therefore, it is necessary to compile tacit, disseminated and complex knowledge from various Tradi-Practitioners (TP) in order to determine interesting patterns for treating a given disease. Knowledge engineering methods for traditional medicine are useful to model suitably complex information needs, formalize knowledge of domain experts and highlight the effective practices for their integration to conventional medicine. The work described in this paper presents an approach which addresses two issues. First it aims at proposing a formal representation model of ATM knowledge and practices to facilitate their sharing and reusing. Then, it aims at providing a visual reasoning mechanism for selecting best available procedures and medicinal plants to treat diseases. The approach is based on the use of the Delphi method for capturing knowledge from various experts which necessitate reaching a consensus. Conceptual graph formalism is used to model ATM knowledge with visual reasoning capabilities and processes. The nested conceptual graphs are used to visually express the semantic meaning of Computational Tree Logic (CTL) constructs that are useful for formal specification of temporal properties of ATM domain knowledge. Our approach presents the advantage of mitigating knowledge loss with conceptual development assistance to improve the quality of ATM care (medical diagnosis and therapeutics), but also patient safety (drug monitoring).     

中药复方配伍多维分析系统中数据库的设计

为了揭示中药复方配伍规律,开发了中药复方配伍多维分析系统.由于中药数据涉及大量自然、人文知识,在系统中需要处理这些错综复杂的关系,因此设计存储这些复杂数据的数据库是个挑战性的工作.给出了中药复方配伍多维分析系统的分析流程,解释了中药信息的特点,详细分析了存储中药信息数据库子系统的设计.把存储子系统数据库子系统分为输入表、输出表、药物基本信息表、预处理表和临时表5类,使系统具有灵活性,可扩展性,较少的冗余、较快的处理速度,满足了多维数据分析的需要.     

Architecture for knowledge discovery and knowledge management

In this paper, we propose I-MIN model for knowledge discovery and knowledge management in evolving databases. The model splits the KDD process into three phases. The schema designed during the first phase, abstracts the generic mining requirements of the KDD process and provides a mapping between the generic KDD process and (user) specific KDD subprocesses. The generic process is executed periodically during the second phase and windows of condensed knowledge called knowledge concentrates are created. During the third phase, which corresponds to actual mining by the end users, specific KDD subprocesses are invoked to mine knowledge concentrates. The model provides a set of mining operators for the development of mining applications to discover and renew, preserve and reuse, and share knowledge for effective knowledge management. These operators can be invoked by either using a declarative query language or by writing applications.The architectural proposal emulates a DBMS like environment for the managers, administrators and end users in the organization. Knowledge management functions, like sharing and reuse of the discovered knowledge among the users and periodic updating of the discovered knowledge are supported. Complete documentation and control of all the KDD endeavors in an organization are facilitated by the I-MIN model. This helps in structuring and streamlining the KDD operations in an organization.     

Using MineSet for knowledge discovery

MineSet aids knowledge discovery and supports decision making based on relational data. It uses visualization and data mining to arrive at interesting results. Providing diverse visualization tools lets users choose the most appropriate method for a given problem. The client-server architecture performs most of the computationally intensive tasks on a server, while the processed results return to the client for visualization. The paper discusses MineSet database visualization and data mining visualization     

New construction for expert system based on innovative knowledge discovery technology

Knowledge acquisition is the bottleneck of expert system. To solve this problem, KD (D&K), which is a comprehensive knowledge discovery process model cooperating both database and knowledge base, and related technology are proposed. Then based on KD (D&K) and related technology, the new construction of Expert System based on Knowledge Discovery (ESKD) is proposed. As the key knowledge acquisition component of ESKD, KD (D&K) is composed of KDD* and KDK*. KDD*—the new process model based on double bases cooperating mechanism; KDK*—the new process model based on double-basis fusion mechanism are introduced, respectively. The overall framework of ESKD is proposed. Some sub-systems and dynamic knowledge base system are discussed. Finally, the effectiveness and advantages of ESKD are tested in a real-world agriculture database. We hope that ESKD may be useful for the new generation of expert systems. Supported by the National Natural Science Foundation of China (Grant No. 69835001), the Ministry of Education of China (Grant No. [2000] 175), and the Science Foundation of Beijing (Grant No. 4022008).     

基于Web的中药化学数据库

为了研究中药以及通过WWW来交流中药的相关信息,开发了中药化学数据库和基于Web的检索程序。本文简单介绍了中药化学数据库,说明了在开发程序中所使用的各种技术,包括ASP、VRML和JavaScript。另外还给出了一些应用实例。     

Latent class model based diagnostic system utilizing traditional Chinese medicine for patients with systemic lupus erythematosus

Systemic lupus erythematosus (SLE) can affect nearly any organ system, and is frequently an evolving disease with varied manifestations. Traditional Chinese medicine (TCM) physicians have identified different SLE patterns that they have difficulty summarizing, but the latent class model helps solve this problem. This study applies the latent class model and disease pattern coding system (B-code) to design a TCM diagnostic expert system. This study gathered 2047 valid records and classified three clusters of main disease patterns. Compared with the experience of the TCM expert, the accuracy rate of the expert system reached 77.47%. The results show that this diagnostic system performed well in identifying the disease patterns of SLE and may be clinically useful for TCM physicians.     

Streaming-media knowledge discovery

As the amount of streaming audio and video available to World Wide Web users grows, tools for analyzing and indexing this content will become increasingly important. Frequently, knowledge management applications and information portals synthesize unstructured text information from the Web, intranets and partner sites. Given this context, we crawl a statistically significant number of Web pages, detect those that contain streaming media links, crawl the media links to extract associated meta-data, then use the crawl data to build a resource list for Web media. We have used these crawl-data findings to build a media indexing application that uses content-based indexing methods     

抗缺氧中藏药微量元素的对应分析

应用对应分析研究抗缺氧中藏药中微量元素Co、Cr、Cu、Fe、Mg、Mn、Ni and Zn的分布特征。该方法可用于直接处理抗缺氧中藏药样品元素分析的数据,以研究地奥心血康、红景天胶囊、安神散、五味石榴散、章松八味沉香散、十六味杜鹃散、二十五味余甘子丸、二十五味竹黄胶囊、十五味龙胆丸、秘诀清凉散、七十味珍珠丸、八味沉香散、六味丁香散、七味葡萄散等中藏药的品质与它们的微量元素组成和样品来源的关系。将样品点和微量元素变量反映到同一因子轴的图形上,并且分类,能够揭露出样品来源与变量之间的内在联系,中藏药中人体必需的微量元素Fe、Mg、Co、Cu、Mn和Ni等含量比较高,与药物疗效密切相关,为研制新型的具有微量元素优势的药物提供科学的依据。     

Systems for knowledge discovery in databases

Knowledge-discovery systems face challenging problems from real-world databases, which tend to be dynamic, incomplete, redundant, noisy, sparse, and very large. These problems are addressed and some techniques for handling them are described. A model of an idealized knowledge-discovery system is presented as a reference for studying and designing new systems. This model is used in the comparison of three systems: CoverStory, EXPLORA, and the Knowledge Discovery Workbench. The deficiencies of existing systems relative to the model reveal several open problems for future research     

基于知识发现的网络安全态势感知系统

网络安全态势感知主要是站在整体大局的立场上去动态反映信息网络的安全环境,并从根本上对信息网络安全的发展趋势做出了评估和分析.作为网络安全领域的新热点,网络安全态势感知在学术的研究中取得了广泛的进展,但在系统构建和应用方面发展还不够迅速.在此背景下,提出了一种基于知识发现的网络安全态势建模和生成框架,通过一系列的实验过程以及结果分析,表明了该系统能够支持网络安全态势感知的不断发展.     

Data-intensive architecture for scientific knowledge discovery

This paper presents a data-intensive architecture that demonstrates the ability to support applications from a wide range of application domains, and support the different types of users involved in defining, designing and executing data-intensive processing tasks. The prototype architecture is introduced, and the pivotal role of DISPEL as a canonical language is explained. The architecture promotes the exploration and exploitation of distributed and heterogeneous data and spans the complete knowledge discovery process, from data preparation, to analysis, to evaluation and reiteration. The architecture evaluation included large-scale applications from astronomy, cosmology, hydrology, functional genetics, imaging processing and seismology.     

采用过程分析技术的中药质量控制系统方案设计

中药产业是我国医药工业的重要组成部分,已被列入国家战略性新兴产业,但是当前中药制药的工艺水平仍比较低,缺乏过程质量监控手段,阻碍了中药制药质量的进一步提升.文章通过采用质量源于设计的理念,研究中药生产过程中关键工序的工艺参数对产品质量的影响,采用过程分析技术设计了一套中药质量控制方案,对整个流程的数据进行监测与分析,有效地解决了中药质量复杂性和质量控制系统性难题.     

中药制药过程控制系统设计研究

本文以某中药制药厂生产过程控制系统为研究对象,分析了中药生产各工段的工艺设备和控制特点.针对控制系统设备集成化程度差.自动化水平低的缺点,设计了基于PROFIBUS-DP的现场总线控制网络,介绍了控制系统的硬件配置、系统组态、软件体系.利用PROFIBUS和工业以太网的结合,实现了对中药制药各工段的集中监控、分散控制,为先进智能控制在中药制药过程中的应用构建了平台.     

A domain-specific decision support system for knowledge discovery using association and text mining

We propose a novel association and text mining system for knowledge discovery (ASTEK) from the warranty and service data in the automotive domain. The complex architecture of modern vehicles makes fault diagnosis and isolation a non-trivial task. The association mining isolates anomaly cases from the millions of service and claims records. ASTEK has shown 86% accuracy in correctly identifying the anomaly cases. The text mining subscribes to the diagnosis and prognosis (D&P) ontology, which provides the necessary domain-specific knowledge. The root causes associated with the anomaly cases are identified by discovering frequent symptoms associated with the part failures along with the repair actions used to fix the part failures. The best-practice knowledge is disseminated to the dealers involved in the anomaly cases. ASTEK has been implemented as a prototype in the service and quality department of GM and its performance has been validated in the real life set up. On an average, the analysis time is reduced from few weeks to few minutes, which in real life industry are significant improvements.     

Complex relationships and knowledge discovery support in the InfoQuilt system

Support for semantic content is becoming more common in Web-accessible information systems. We see this support emerging with the use of ontologies and machine-readable, annotated documents. The practice of domain modeling coupled with the extraction of domain-specific, contextually relevant metadata also supports the use of semantics. These advancements enable knowledge discovery approaches that define complex relationships between data that is autonomously collected and managed. The InfoQuilt (One of the incarnations of the InfoQuilt system, as applied to the geographic information as part of the NSF Digital Library II initiative is the ADEPT-UGA system [Ade]. This research was funded in part by National Science Foundation grant IIS-9817432.) system supports one such knowledge discovery approach. This paper presents (parts of) the InfoQuilt system with the focus on its use for modeling and utilizing complex semantic inter-domain relationships to enable human-assisted knowledge discovery over Web-accessible heterogeneous data. This includes the specification and execution of Information Scale (IScapes), a semantically rich information request and correlation mechanism. Edited by: L. Liu. Received: October 28, 2001 / Accepted: June 1, 2002Published online: September 25, 2002