RHINOS: a consultation system for diagnosis of headache and facial pain

This paper is intended to describe the expert system RHINOS for diagnosing patients whose chief complaints are headache and facial pain. RHINOS assists physicians in diagnosing the above-mentioned disease. For this purpose the authors surveyed diagnosing processes used by physicians to determine what advice physicians want. As a result, the author reached the conclusion that knowledge for diagnosing the above-mentioned disease should be categorized according to four types of rules: exclusive rules (if the patient has disease D, he must have symptoms S1, S2,....,Sn), inclusive rules (if the patient has symptoms S1, S2,...,Sn, he has disease D with a probability X (0 less than X less than 1)), associate rules (if the patient has symptoms S1, S2,...,Sn the probability that he has the disease D increases) and disease image (if the patient has disease D, he may have symptoms S1, S2,...,Sn). Through harmonious use of these four types of rules, RHINOS gives the advise that physicians want. RHINOS is widely available because it is implemented by Prolog-KABA which is operative on personal computers such as NEC PC9801 and IBM-PC.     

A programming language and a system for automated time- and laboratory test level dependent decision-making during patient monitoring

A programming language specifically designed for automated time-dependent decision-making during patient monitoring (obtaining test requests, scheduling of test requests, cancelling of test requests, the issuing of messages to clinician or laboratory) has been developed. The output from a program written in this language is a set of decision rules in tabular form (a patient type), intended to solve a specific problem related to patient monitoring. A system for automated decision-making based on this language utilizes a file of patient types and a patient file. Whenever a new patient record is created, the patient's type is defined and the corresponding patient type is copied from the patient type file and linked to the patient's record. The system is activated, i.e., the decision rules are implemented and the tables of the patient's type updated, when laboratory results are available, when a clinician wants to order tests, and at regular user-defined intervals.     

Computer probability estimates of angiographic coronary artery disease: transportability and comparison with cardiologists' estimates.

A computer algorithm for estimating probabilities of any significant coronary obstruction and triple vessel/left main obstructions was derived, validated, and compared with the assessments of cardiac clinician angiographers. The algorithm performed at least as well as the clinicians when the latter knew the identity of the patients whose angiograms they had decided to perform. The clinicians were more accurate when they did not know the identity of the subjects but worked from tabulated objective data. Referral and value induced bias may affect physician judgment in assessing disease probability. Application of computer aids or consultation with cardiologists not directly involved with patient management may assist in more rational assessments and decision making.     

Reliability Parameters to Improve Combination Strategies in Multi-Expert Systems

Recognition systems based on a combination of different experts have been widely investigated in the recent past. General criteria for improving the performance of such systems are based on estimating the reliability associated with the decision of each expert, so as to suitably weight its response in the combination phase. According to the methods proposed to-date, when the expert assigns a sample to a class, the reliability of such a decision is estimated on the basis of the recognition rate obtained by the expert on the chosen class during the training phase. As a consequence, the same reliability value is associated with every decision attributing a sample to a same class, even though it seems reasonable to take into account its dependence on the quality of the specific sample. We propose a method for estimating the reliability of each single recognition act of an expert on the basis of information directly derived from its output. In this way, the reliability value of a decision is more properly estimated, thus allowing a more precise weighting during the combination phase. The definition of the reliability parameters for widely used classification paradigms is discussed, together with the combining rules employing them for weighting the expert opinions. The results obtained by combining four experts in order to recognise handwritten numerals from a standard character database are presented. Comparison with classical combining rules is also reported, and the advantages of the proposed approach outlined. Received: 3 August 1997?Received in revised form: 24 November 1998?Accepted: 11 December 1998     

Constructing a nutrition diagnosis expert system

This paper presents a research of constructing a web-based expert system for nutrition diagnosis by utilizing the expert system techniques in artificial intelligence. The research implements Nutritional Care Process and Model (NCPM) defined by American Dietetic Association (ADA) in 2008 and integrate the nutrition diagnosis knowledge from dietetics professionals to establish the basics of building the rule-based expert system with its knowledge base. The system is built using Microsoft Visual Studio 2008 on .NET Framework 3.5SP1 utilizing the built in rule engine which comes with Windows Workflow Foundation.With the help of this system, it is easier for dietetics professionals to adapt to the newly introduced concept of nutrition diagnosis. At the heart of the web based expert system is a knowledge base, it has a rule engine which contains the nutrition diagnosis rules converted from signs and symptoms for nutrition diagnosis from dietetics professionals and are expressed in XML format which are then stored in a SQL database. A knowledge engineer will be able to use a rule editor to add new rules or to update existing rules within the rule database. Dietetics professionals would be able to enter patient’s basic data, anthropometric data, physical exam findings, biochemical data, and food/nutrition history into the program. After dietetics professionals complete nutrition assessment, the program will make inference to the rule base and make nutrition diagnosis. Dietetics professionals could then make the final diagnosis decision for the patient based on the diagnosis report generated by the web based nutrition diagnosis expert system.For this study, I have selected 100 chronic kidney disease patients under hemodialysis from a university hospital, recorded their albumin, cholesterol, creatinine before dialysis, height, and dry weight and then use these data to perform nutrition diagnosis with both the expert system and a practicing dietitian. After comparing the result, I found that the expert system is faster and more accurate than human dietitian.     

Evaluation of the Use of Induction in the Development of a Medical Expert System

In an earlier study, two medical expert systems for diagnosing thyroid disorders, developed by the application of induction on a sample of previously diagnosed cases and on expert-generated rules, diagnosed a set of test cases better than an expert system developed by the more traditional method of collaboration between a knowledge engineer and an expert. In this paper, an alternative measure of the accuracy of diagnosis of each system is used to evaluate the systems. Diagnoses for every distinct case represented by a combination of indicating factors are compared with diagnoses that the expert made. The induced systems provide diagnoses for many more distinct cases, but a much higher proportion of these diagnoses are incorrect. It is argued that generalizing to unseen cases is an inappropriate use of induction algorithms. The systematic development of a decision table is a more appropriate method for devising a medical expert system.     

基于专家知识和决策树的设备状态诊断方法

实践了基于专家知识和决策树的设备状态诊断方法。利用专家知识,一方面对样本数据属性进行裁剪,另一方面对正常运行中不易发生的边缘样本点进行人工构造,从而形成一个较完整的样本数据集;利用决策树算法进行规则提取,基于该树形规则,可实现快速状态诊断。     

A data analysis decision support system for the carbon dioxide capture process

This paper presents the development process of an expert decision support system for pre-filtering and analysis of data from the carbon dioxide (CO₂) capture process. Chemical absorption has become one of the dominant CO₂ capture technologies because of its efficiency and low cost. Since the chemical absorption process consists of dozens of components, it generates more than a 100 different types of data. Monitoring the vast amount of data can be complex, and data filtering and analysis processes are desirable. Specifically, invalid data captured as the equipment is started and shut down need to be filtered, and the filtered data need to be analyzed for different purposes. The expert decision support system for data pre-filtering and analysis not only filters out invalid data using different expert rules, but it can also modify or reuse filtering settings, and export the filtered data to various file formats for further analysis. During development of the expert decision support system, knowledge acquisition was emphasized. The system development process incorporated various technologies including the model-view-control (MVC) design pattern, the embedded database technology, the Java event delivery techniques and the eXtensible Markup Language (XML). Some sample sessions from system executions and some results generated from pre-filtering the data will also be discussed.     

A medical decision support system for disease diagnosis under uncertainty

This paper presents a decision support system (DSS) modeled by a fuzzy expert system (FES) for medical diagnosis to help physicians make better decisions. The proposed system collects comprehensive information about a disease from a group of experts. To this aim, a cross-sectional study is conducted by asking physicians’ expertise on all symptoms relevant to a disease. A fuzzy rule based system is then formed based on this information, which contains a set of significant symptoms relevant to the suspected disease. Linguistic fuzzy values are assigned to model each symptom. The input of the system is the severity level of each symptom reported by patients. The proposed FES considers two approaches to account for uncertain inputs from patients. Two case studies on kidney stone and kidney infection were conducted to demonstrate how the proposed method could be used. A group of patients were used to validate the effectiveness of the proposed expert system. The results show that the proposed fuzzy expert system is capable of diagnosing diseases with a high degree of accuracy and precision comparing to a couple of machine learning methods.     

带Rough算子的决策规则及数据挖掘中的软计算

文中讨论决策规则及其与演绎推理中的假言推理规则之间的关系,通过数据挖掘中的软计算使决策表中的属性简化和性值区间化,从而找到一种具有广泛表达能力的数据隐含格式,从中选择有代表性的,并删去冗余或过剩的规则,并保持决策表的原有用途和的有性能,我们通过开发一个中医诊疗专家系统的实例说明了这种软计算的过程,并分别用于统计或专家计算带可信度因子的产生式规则和基于Ｒｏｕｇｈ集方法计算带Ｒｏｕｇｈ算子的决策规则两     

Predicting Corporate Acquisitions: An Application of Uncertain Reasoning Using Rule Induction

Artificial Intelligence (AI)-based rule induction techniques such as IXL and ID3 are powerful tools that can be used to classify firms as acquisition candidates or not, based on financial and other data. The purpose of this paper is to develop an expert system that employs uncertainty representation and predicts acquisition targets. We outline in this paper, the features of IXL, a machine learning technique that we use to induce rules. We also discuss how uncertainty is handled by IXL and describe the use of confidence factors. Rules generated by IXL are incorporated into a prototype expert system, ACQTARGET, which evaluates corporate acquisitions. The use of confidence factors in ACQTARGET allows investors to specifically incorporate uncertainties into the decision making process. A set of training examples comprising 65 acquired and 65 non-acquired real world firms is used to generate the rules and a separate holdout sample containing 32 acquired and 32 non-acquired real world firms is used to validate the expert system results. The performance of the expert system is also compared with a conventional discriminant analysis model and a logit model using the same data. The results show that the expert system, ACQTARGET, performs as well as the statistical models and is a useful evaluation tool to classify firms into acquisition and non-acquisition target categories. This rule induction technique can be a valuable decision aid to help financial analysts and investors in their buy/sell decisions.     

Development of an expert system for optimizing natural gas pipeline operations

This paper presents an expert system as a decision support tool to optimize natural gas pipeline operations. A natural gas pipeline control system is a controlling system that involves many complicated operating processes. Since a dispatcher (who operates the system) might not be able to handle all of his or her tasks consistently, an expert system has been developed for optimizing the operations by providing consistent, fast and reliable decision support to the dispatcher. Consequently, inconsistency in the dispatcher's performance can be minimized. To build an expert system, the knowledge from an experienced dispatcher, who is familiar with the process in this controlling system is acquired and that knowledge has been implemented as rules in the knowledge base of the expert system. When this expert system has been validated by gas pipeline experts, it can help inexperienced dispatchers to operate the processes more effectively. The expert system is implemented on the real-time expert system shell G2 (trademark of Gensym Corp. of USA). The system also consists of a user interface that helps dispatchers visualize system conditions.     

Using fuzzy sets to diagnose coronary artery stenosis

The use of fuzzy sets to represent perfusion defects and to generate expert results to help in diagnosis is reported. Retrospective data collected from 91 patients who underwent both stress thallium-201 myocardial scintigraphy and coronary arteriography were used. Of the total, 64 scans were chosen at random for training, and the remaining 27 scans were used for testing data. It was found that 17 rules generated by fuzzy set theory performed as well as 68 rules specified by cardiologists in diagnosing coronary artery stenosis     

Creating Decision Trees from Rules using RBDT‐1

Most of the methods that generate decision trees for a specific problem use the examples of data instances in the decision tree–generation process. This article proposes a method called RBDT‐1—rule‐based decision tree—for learning a decision tree from a set of decision rules that cover the data instances rather than from the data instances themselves. The goal is to create on demand a short and accurate decision tree from a stable or dynamically changing set of rules. The rules could be generated by an expert, by an inductive rule learning program that induces decision rules from the examples of decision instances such as AQ‐type rule induction programs, or extracted from a tree generated by another method, such as the ID3 or C4.5. In terms of tree complexity (number of nodes and leaves in the decision tree), RBDT‐1 compares favorably with AQDT‐1 and AQDT‐2, which are methods that create decision trees from rules. RBDT‐1 also compares favorably with ID3 while it is as effective as C4.5 where both (ID3 and C4.5) are well‐known methods that generate decision trees from data examples. Experiments show that the classification accuracies of the decision trees produced by all methods under comparison are indistinguishable.     

Using rule induction for knowledge acquisition: An expert systems approach to evaluating material errors and irregularities

There has been a significant increase in the magnitude of material errors discovered in financial statements during the 1980s. Auditors, financial analysts, and regulators have shown considerable interest in evaluating and predicting these material errors. This paper describes the development and validation of a prototype expert system, ERRORXPERT, which evaluates material errors and potential fraud. This prototype system is designed to assist auditors at the planning stage in the design of subsequent substantive tests, when material errors and irregularities in the financial statements are probable. A commercial machine learning program was used for rule induction. A set of training examples comprising error and non-error firms was used to generate rules and a separate holdout sample was used to validate the expert system results. The performance of the expert system was also compared to that of a multiple discriminant analysis model using the same data. The results demonstrate that the expert system, ERRORXPERT, outperforms the discriminant model and is a powerful evaluation tool to classify firms into error and non-error categories. The size of the sample used in this study somewhat limits the generalizability of the specific rules.     

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).     

Transfer: An expert system approach to transfer pricing

This paper explores the development of a PC-based expert system for transfer price decision making. The expert system (TRANSFER) is based on the premise that, if the corporate goal structure, market structure, costing systems, and other relevant variables are known, it is possible to arrive at the appropriate transfer price by applying a set of optimization rules. VP-Expert was the PC-based expert system shell used to develop TRANSFER. VP-Expert uses backward chaining to solve problems using IF-THEN rules. TRANSFER was designed, tested, and modified to yield the appropriate transfer pricing strategy in a variety of decision scenarios.     

Knowledge acquisition in incomplete fuzzy information systems via the rough set approach

Abstract: Machine learning can extract desired knowledge from training examples and ease the development bottleneck in building expert systems. Most learning approaches derive rules from complete and incomplete data sets. If attribute values are known as possibility distributions on the domain of the attributes, the system is called an incomplete fuzzy information system. Learning from incomplete fuzzy data sets is usually more difficult than learning from complete data sets and incomplete data sets. In this paper, we deal with the problem of producing a set of certain and possible rules from incomplete fuzzy data sets based on rough sets. The notions of lower and upper generalized fuzzy rough approximations are introduced. By using the fuzzy rough upper approximation operator, we transform each fuzzy subset of the domain of every attribute in an incomplete fuzzy information system into a fuzzy subset of the universe, from which fuzzy similarity neighbourhoods of objects in the system are derived. The fuzzy lower and upper approximations for any subset of the universe are then calculated and the knowledge hidden in the information system is unravelled and expressed in the form of decision rules.