ARDEN2BYTECODE: a one-pass Arden Syntax compiler for service-oriented decision support systems based on the OSGi platform

Patient empowerment might be one key to reduce the pressure on health care systems challenged by the expected demographic changes. Knowledge based systems can, in combination with automated sensor measurements, improve the patients' ability to review their state of health and make informed decisions. The Arden Syntax as a standardized language to represent medical knowledge can be used to express the corresponding decision rules. In this paper we introduce ARDEN2BYTECODE, a newly developed open source compiler for the Arden Syntax. ARDEN2BYTECODE runs on Java Virtual Machines (JVM) and translates Arden Syntax directly to Java Bytecode (JBC) executable on JVMs. ARDEN2BYTECODE easily integrates into service oriented architectures, like the Open Services Gateway Initiative (OSGi) platform. Apart from an evaluation of compilation performance and execution times, ARDEN2BYTECODE was integrated into an existing knowledge supported exercise training system and recorded training sessions have been used to check the implementation.     

Partitioning knowledge bases between advanced notification and clinical decision support systems

Clinical decision support system (CDSS) and their logic syntax include the coding of notifications (e.g., Arden Syntax). The following paper will describe the rationale for segregating policies, user preferences and clinical monitoring rules into “advanced notification” and” clinical” components, which together form a novel and complex CDSS. Notification rules and hospital policies are respectively abstracted from care-provider roles and alerting mechanisms. User-defined preferences determine which devices are to be used for receiving notifications. Our design differs from previous notification systems because it integrates a versatile notification platform supporting a wide range of mobile devices with a XML/HL-7 compliant communication protocol.     

Development and implementation of clinical guidelines: An artificial intelligence perspective

Clinical practice guidelines in paper format are still the preferred form of delivery of medical knowledge and recommendations to healthcare professionals. Their current support and development process have well identified limitations to which the healthcare community has been continuously searching solutions. Artificial intelligence may create the conditions and provide the tools to address many, if not all, of these limitations.. This paper presents a comprehensive and up to date review of computer-interpretable guideline approaches, namely Arden Syntax, GLIF, PROforma, Asbru, GLARE and SAGE. It also provides an assessment of how well these approaches respond to the challenges posed by paper-based guidelines and addresses topics of Artificial intelligence that could provide a solution to the shortcomings of clinical guidelines. Among the topics addressed by this paper are expert systems, case-based reasoning, medical ontologies and reasoning under uncertainty, with a special focus on methodologies for assessing quality of information when managing incomplete information. Finally, an analysis is made of the fundamental requirements of a guideline model and the importance that standard terminologies and models for clinical data have in the semantic and syntactic interoperability between a guideline execution engine and the software tools used in clinical settings. It is also proposed a line of research that includes the development of an ontology for clinical practice guidelines and a decision model for a guideline-based expert system that manages non-compliance with clinical guidelines and uncertainty.     

An expert decision support system for network routing: a case study

The paper presents a case study of the development of an expert decision support system which uses simple heuristic methods for fast determination of routes for simultaneous signals in a transmission network of limited capacity. It illustrates how heuristic solutions can be embodied in a model-based DSS and how the standard decision support literature, although intuitively appealing, provides little practical assistance in system construction or classification     

Design of a Clinical Event Monitor

The issues and implementation of a clinical event monitor are described. An event monitor generates messages for providers, patients, and organizations based on clinical events and patient data. For example, an order for a medication might trigger the generation of a warning about a drug interaction. A model based on the active database literature has as its main components an event (which triggers a rule to fire), a condition (which tests whether an action ought to be performed), and an action (often the generation of a message). The details of implementing such a monitor are described, using as an example the Columbia-Presbyterian Medical Center clinical event monitor, which is based on the Arden Syntax for Medical Logic Modules.     

粒及粒计算在逻辑推理中的应用

讨论了信息粒的结构及其实例。基于Rough集方法定义了决策规则粒,构造了决策规则粒库,它被用作逻辑推理。定义了粒语言,描述了这种语言的语法、语义、粒语句的运算法则和粒之相关的几个性质。定义了粒之间的相互包含(inclusion)和相似(closeness)。基于这些概念,构造了一种逻辑推理的新模型。这种推理模式的特点在于它既是逻辑的又是集合论的。所谓逻辑的就是说推理是遵循一种逻辑运算;所谓集合论的是指这种推理可利用对应于这种逻辑公式的意义集的运算进行推理,还用实例说明了这种推理模式是可行和有效的。     

汉语句法树库检索系统的设计与实现

句法树库是一项重要资源,它能为汉语语言研究和信息处理提供一个有利的数据平台。汉语句法树库检索的实现用到了流操作和GD I+图形操作。汉语句法库检索系统是基于句法树库的应用系统,提供了对句法树库的检索和统计,并且能够根据树库中的合法语句画出该语句的树状结构图,进而使用户对语句的句法结构有一个形象、直观的了解。     

粗糙决策支持方法

粗糙分析方法是从粗糙集理论发展出来的技术之一．传统的粗糙分析方法能够从决策表中获取经过属性约简和值约简的决策规则．这些规则虽然能够提供一定程度的决策支持，但是这些规则仅保留了决策表的部分决策支持能力，在实际的决策过程中，往往无法提供良好的决策支持．对此，该文提出一组用于决策支持的粗糙分析方法，称为粗糙决策支持方法．该方法能够充分挖掘决策表的决策能力，以提供强有力的决策支持，并且本质上提供容错的决策支持．该方法与传统方法能够整合为动静结合的决策支持模式，并提供强大而又快速的决策支持．     

基于缺省规则的决策支持方法

利用Rough集理论的基本原理和方法,在提出一种缺省规则挖掘策略和算法的基础上,系统地描述了基于缺省规则的决策支持方法,将其应用于汽车故障诊断决策分析中.试用表明,该方法能较好地排除噪声的影响,使决策者在有限的时间和有限的知识下,作出比较合理的决策.     

一种带缺省推理的描述逻辑

该文提出了一种新的带缺省推理的描述逻辑，它以描述逻辑为主要框架，对单调逻辑和非单调逻辑进行了整合，但又避免了一般缺省逻辑的困难．基于带缺省推理的描述逻辑，构建了一种同时具有Tbox，Abox和缺省规则的知识库系统，研究了带缺省推理的描述逻辑的可满足性、缺省可满足性、概念包含、缺省包含以及实例检测等推理问题，提出了一种用来检测可满足性和缺省可满足性的Tableau—D算法，并得到了缺省可满足性和缺省包含的转换定理．     

基于粗糙信息向量的一种决策规则获取算法

针对不一致信息系统中决策规则获取问题,提出了一种基于粗糙信息向量方法的决策规则挖掘算法。基于粗糙信息向量,利用条件向量对决策向量的决策支持能力,直接从决策表中挖掘出符合阈值要求的尽可能简洁的决策规则,且不损失条件属性值的决策支持能力。利用该算法可以挖掘出决策系统中条件属性在各个简化层次情况下的确定性规则和缺省规则集合。理论分析和实例表明该算法在不一致信息系统中的决策规则获取上是可行的。     

一种基于Rough集获取规则知识的增量式学习方法

将Rough集理论应用于规则归纳系统,提出了一种基于粗糙集获取规则知识库的增量式学习方法,能够有效处理决策表中不一致情形,采用启发式算法获取决策表的最简规则,当新对象加入时在原有规则集基础上进行规则知识库的增量式更新,避免了为更新规则而重新运行规获取算法。并用UCI中多个数据集从规则集的规则数目、数据浓缩率、预测能力等指标对该算法进行了测试。实验表明了该算法的有效性。     

Development and maintenance of guideline-based decision support for pharmacological treatment of hypertension

The objective was to build a computer-based decision support system (DSS), which could apply the formal rules embedded in guidelines regarding pharmacological treatment of hypertension. The aim was also to test VISUAL BASIC as a development tool for DSS's in health care. From the Swedish guidelines for treatment of hypertension, the most widely accepted and scientifically best proved treatment strategies were chosen and implemented as rules. A DSS that is capable of applying the evidence-based rules extracted from guidelines regarding drug treatment of hypertension, to any patient's medical profile, was constructed. The output consists of a recommendation regarding preferred generic drug class and also a written report, reflecting decision steps provided by the rule-base and inference engine. We also provide methods for formalising an implementable language of guidelines. A mainstream programming language like VISUAL BASIC can be an alternative when building complicated decision support systems. A logic formal notation can facilitate communication between the expert and the programmer. The program is a stand-alone product independent of computerized medical records and thereby easy to install and maintain.     

Empirical evaluation of a decision support model for adopting software product line engineering

ContextThe software product line engineering (SPLE) community has provided several different approaches for assessing the feasibility of SPLE adoption and selecting transition strategies. These approaches usually include many rules and guidelines which are very often implicit or scattered over different publications. Hence, for the practitioners it is not always easy to select and use these rules to support the decision making process. Even in case the rules are known, the lack of automated support for storing and executing the rules seriously impedes the decision making process.ObjectiveWe aim to evaluate the impact of a decision support system (DSS) on decision-making in SPLE adoption. In alignment with this goal, we provide a decision support model (DSM) and the corresponding DSS.MethodFirst, we apply a systematic literature review (SLR) on the existing primary studies that discuss and present approaches for analyzing the feasibility of SPLE adoption and transition strategies. Second, based on the data extraction and synthesis activities of the SLR, the required questions and rules are derived and implemented in the DSS. Third, for validation of the approach we conduct multiple case studies.ResultsIn the course of the SLR, 31 primary studies were identified from which we could construct 25 aspects, 39 questions and 312 rules. We have developed the DSS tool Transit-PL that embodies these elements.ConclusionsThe multiple case study validation showed that the adoption of the developed DSS tool is justified to support the decision making process in SPLE adoption.     

Comparison of intelligent systems in detecting a child’s mathematical gift

This paper compares the efficiency of two intelligent methods: expert systems and neural networks, in detecting children’s mathematical gift at the fourth grade of elementary school. The input space for the expert system and the neural network model consisted of 60 variables describing five basic components of a child’s mathematical gift identified in previous research. The expert system estimated a child’s gift based on heuristically defined logic rules, while the scientifically confirmed psychological evaluation of gift based on Raven’s standard progressive matrices was used at the output of neural network models. Three neural network algorithms were tested on a Croatian dataset. The results show that both the expert system and the neural network recognize more pupils as mathematically gifted than teachers do. The expert system produces the highest average hit rate, although the highest accuracy in classifying gifted children is obtained by the radial basis neural network algorithm, which also yields lower type II error. Due to the ability of expert systems to explain the result, it can be suggested that both the expert system and the neural network model have potential to serve as effective intelligent decision support tools in detecting mathematical gift in early stage, therefore enabling its further development.     

序区间值决策系统中最优可信规则获取与约简

基于描述子的规则获取可导出序值决策系统中的所有可信规则,但对包含区间值序决策系统却不能有效支持。因此,首先根据每个属性值域的范围,提出了一个区间段值的概念,用以将序区间值决策系统转换为序区间段值决策系统;然后,在序区间段值决策系统中提出了基于区间段值的优势和弱势描述子概念,用以导出序区间值决策系统中的所有可信规则;最后,研究了两种新的描述子的约简以及相对约简问题,给出了相应的判定定理与区分函数。以上为从序区间值决策系统中获取有效的最优可信决策规则提供了一种新理论基础与操作手段。     

Realizing active data warehouses with off‐the‐shelf database technology

Active data warehouses belong to a new category of decision support systems, which automate decision making for routine decision tasks and semi‐routine decision tasks. Just as active database systems extend conventional database systems with event–condition–action rules for integrity constraint enforcement or procedure execution, active data warehouses extend conventional data warehouses with analysis rules that mimic the work of an analyst during decision making. This paper demonstrates how analysis rules can be implemented on top of a passive relational data warehouse system by using commercially available database technology. Copyright © 2002 John Wiley & Sons, Ltd.     

A knowledge-based decision support system for the management of parts and tools in FMS

Flexible manufacturing systems (FMS) are very complex systems with large part, tool, and information flows. The aim of this work is to develop a knowledge-based decision support system (KBDSS) for short-term scheduling in FMS strongly influenced by the tool management concept to provide a significant operational control tool for a wide range of machining cells, where a high level of flexibility is demanded, with benefits of more efficient cell utilization, greater tool flow control, and a dependable way of rapidly adjusting short-term production requirements. Development of a knowledge-based system to support the decision making process is justified by the inability of decision makers to diagnose efficiently many of the malfunctions that arise at machine, cell, and entire system levels during manufacturing. In this context, this paper proposes three knowledge-based models to ease the decision making process: an expert production scheduling system, a knowledge-based tool management decision support systems, and a tool management fault diagnosis system. The entire system has been created in a hierarchical manner and comprises more than 400 rules. The expert system (ES) was implemented in a commercial expert system shell, Knowledge Engineering System (KES) Production System (PS).     

Using a fuzzy association rule mining approach to identify the financial data association

In the rapidly changing financial market, investors always have difficulty in deciding the right time to trade. In order to enhance investment profitability, investors desire a decision support system. The proposed artificial intelligence methodology provides investors with the ability to learn the association among different parameters. After the associations are extracted, investors can apply the rules in their decision support systems. In this work, the model is built with the ultimate goal of predicting the level of the Hang Seng Index in Hong Kong. The movement of Hang Seng Index, which is associated with other economics indices including the gross domestic product (GDP) index, the consumer price index (CPI), the interest rate, and the export value of goods from Hong Kong, is learnt by the proposed method. The case study shows that the proposed method is a feasible way to provide decision support for investors who may not be able to identify the hidden rules between the Hang Seng Index and other economics indices.     

Use of conditional rule structure to automate clinical decision support: a comparison of artificial intelligence and deterministic programming techniques

A rule-based computer system was developed to perform clinical decision-making support within a medical information system, oncology practice, and clinical research. This rule-based system, which has been programmed using deterministic rules, possesses features of generalizability, modularity of structure, convenience in rule acquisition, explanability, and utility for patient care and teaching, features which have been identified as advantages of artificial intelligence (AI) rule-based systems. Formal rules are primarily represented as conditional statements; common conditions and actions are stored in system dictionaries so that they can be recalled at any time to form new decision rules. Important similarities and differences exist in the structure of this system and clinical computer systems utilizing artificial intelligence (AI) production rule techniques. The non-AI rule-based system possesses advantages in cost and ease of implementation. The degree to which significant medical decision problems can be solved by this technique remains uncertain as does whether the more complex AI methodologies will be required.