CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Retrieval, reuse, revision and retention in case-based reasoning

El original está disponible en www.journals.cambridge.orgCase-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief overview of the traditional problem-solving cycle in CBR, we examine the cognitive science foundations of CBR and its relationship to analogical reasoning. We then review a representative selection of CBR research in the past few decades on aspects of retrieval, reuse, revision, and retention.Peer reviewe

Alternative sweetener from curculigo fruits

This study gives an overview on the advantages of Curculigo Latifolia as an alternative sweetener and a health product. The purpose of this research is to provide another option to the people who suffer from diabetes. In this research, Curculigo Latifolia was chosen, due to its unique properties and widely known species in Malaysia. In order to obtain the sweet protein from the fruit, it must go through a couple of procedures. First we harvested the fruits from the Curculigo trees that grow wildly in the garden. Next, the Curculigo fruits were dried in the oven at 50 0C for 3 days. Finally, the dried fruits were blended in order to get a fine powder. Curculin is a sweet protein with a taste-modifying activity of converting sourness to sweetness. The curculin content from the sample shown are directly proportional to the mass of the Curculigo fine powder. While the FTIR result shows that the sample spectrum at peak 1634 cm–1 contains secondary amines. At peak 3307 cm–1 contains alkynes

Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

Integrating case based reasoning and geographic information systems in a planing support system: Çeşme Peninsula study

Thesis (Doctoral)--Izmir Institute of Technology, City and Regional Planning, Izmir, 2009Includes bibliographical references (leaves: 110-121)Text in English; Abstract: Turkish and Englishxii, 140 leavesUrban and regional planning is experiencing fundamental changes on the use of of computer-based models in planning practice and education. However, with this increased use, .Geographic Information Systems. (GIS) or .Computer Aided Design.(CAD) alone cannot serve all of the needs of planning. Computational approaches should be modified to deal better with the imperatives of contemporary planning by using artificial intelligence techniques in city planning process.The main aim of this study is to develop an integrated .Planning Support System. (PSS) tool for supporting the planning process. In this research, .Case Based Reasoning. (CBR) .an artificial intelligence technique- and .Geographic Information Systems. (GIS) .geographic analysis, data management and visualization techniqueare used as a major PSS tools to build a .Case Based System. (CBS) for knowledge representation on an operational study. Other targets of the research are to discuss the benefits of CBR method in city planning domain and to demonstrate the feasibility and usefulness of this technique in a PSS. .Çeşme Peninsula. case study which applied under the desired methodology is presented as an experimental and operational stage of the thesis.This dissertation tried to find out whether an integrated model which employing CBR&GIS could support human decision making in a city planning task. While the CBS model met many of predefined goals of the thesis, both advantages and limitations have been realized from findings when applied to the complex domain such as city planning

Towards case-based medical learning in radiological decision making using content-based image retrieval

<p>Abstract</p> <p>Background</p> <p>Radiologists' training is based on intensive practice and can be improved with the use of diagnostic training systems. However, existing systems typically require laboriously prepared training cases and lack integration into the clinical environment with a proper learning scenario. Consequently, diagnostic training systems advancing decision-making skills are not well established in radiological education.</p> <p>Methods</p> <p>We investigated didactic concepts and appraised methods appropriate to the radiology domain, as follows: (i) Adult learning theories stress the importance of work-related practice gained in a team of problem-solvers; (ii) Case-based reasoning (CBR) parallels the human problem-solving process; (iii) Content-based image retrieval (CBIR) can be useful for computer-aided diagnosis (CAD). To overcome the known drawbacks of existing learning systems, we developed the concept of image-based case retrieval for radiological education (IBCR-RE). The IBCR-RE diagnostic training is embedded into a didactic framework based on the Seven Jump approach, which is well established in problem-based learning (PBL). In order to provide a learning environment that is as similar as possible to radiological practice, we have analysed the radiological workflow and environment.</p> <p>Results</p> <p>We mapped the IBCR-RE diagnostic training approach into the Image Retrieval in Medical Applications (IRMA) framework, resulting in the proposed concept of the IRMAdiag training application. IRMAdiag makes use of the modular structure of IRMA and comprises (i) the IRMA core, i.e., the IRMA CBIR engine; and (ii) the IRMAcon viewer. We propose embedding IRMAdiag into hospital information technology (IT) infrastructure using the standard protocols Digital Imaging and Communications in Medicine (DICOM) and Health Level Seven (HL7). Furthermore, we present a case description and a scheme of planned evaluations to comprehensively assess the system.</p> <p>Conclusions</p> <p>The IBCR-RE paradigm incorporates a novel combination of essential aspects of diagnostic learning in radiology: (i) Provision of work-relevant experiences in a training environment integrated into the radiologist's working context; (ii) Up-to-date training cases that do not require cumbersome preparation because they are provided by routinely generated electronic medical records; (iii) Support of the way adults learn while remaining suitable for the patient- and problem-oriented nature of medicine. Future work will address unanswered questions to complete the implementation of the IRMAdiag trainer.</p

Case-Based Decision Support for Disaster Management

Disasters are characterized by severe disruptions of the society’s functionality and adverse impacts on humans, the environment, and economy that cannot be coped with by society using its own resources. This work presents a decision support method that identifies appropriate measures for protecting the public in the course of a nuclear accident. The method particularly considers the issue of uncertainty in decision-making as well as the structured integration of experience and expert knowledge

A personalised and adaptive insulin dosing decision support system for type 1 diabetes

People with type 1 diabetes (T1D) rely on exogenous insulin to maintain stable glucose levels. Despite the advent of diabetes technologies such as continuous glucose monitors and insulin infusion pumps, the majority of people with T1D do not manage to bring back glucose levels into a healthy target after meals. In addition to patient compliance, this is due to the complexity of the decision-making on how much insulin is required. Commercial insulin bolus calculators exist that help with the calculation of insulin for meals but these lack fine-tuning and adaptability. This thesis presents a novel insulin dosing decision support system for people with T1D that is able to provide individualised insulin dosing advice. The proposed research utilises Case-Based Reasoning (CBR), an artificial intelligence methodology, that is able to learn over time based on the behaviour of the patient and optimises the insulin therapy for various diabetes scenarios. The decision support system has been implemented into a user-friendly smartphone-based patient platform and communicates with a clinical platform for remote supervision. In-silico studies are presented demonstrating the overall performance of CBR as well as metrics used to adapt the insulin therapy. Safety and feasibility of the developed system have been assessed incrementally in clinical trials; initially during an eight-hour study in hospital settings followed by a six-week study in the home environment of the user. Human factors play an important role in the clinical adoption of technologies such as the one proposed. System usability and acceptability were evaluated during the second study phase based on feedback obtained from study participants. Results from in-silico tests show the potential of the proposed research to safely automate the process of optimising the insulin therapy for people with T1D. In the six-week study, the system demonstrated safety in maintaining glycemic control with a trend suggesting improvement in postprandial glucose outcomes. Feedback from participants showed favourable outcomes when assessing device satisfaction and usability. A six-month large-scale randomised controlled study to evaluate the efficacy of the system is currently ongoing.Open Acces

Improving the evaluation of change requests using past cases

As one of the leading causes of project failures, requirements changes are inevitable in any software project. Hence, we propose an intelligent approach to facilitate the risk analysis of a change request by providing information about past cases found in similar change requests, the solutions adopted, and a support tool. The proposed approach uses case-based reasoning to retrieve previous cases similar to the current case. This approach also uses association rules to analyze patterns in the dataset and calculate the probability of risks associated with change requests. We prepared a case study to validate the proposal by analyzing the most frequent challenges in change management and considering how it can solve or minimize such problems. Results show that the proposed approach successfully assists decision-making, predicts potential risks, and suggests coherent solutions to the user. We have developed a support tool to evaluate this approach in practice with experts and obtained four different outcomes. Only a small set of cases failed to provide relevant results to the user. The use of case-based reasoning and association rules has proven to be advantageous in change management despite validity threats associated with the small number of test cases and experts involved

Improving the evaluation of change requests using past cases

As one of the leading causes of project failures, requirements changes are inevitable in any software project. Hence, we propose an intelligent approach to facilitate the risk analysis of a change request by providing information about past cases found in similar change requests, the solutions adopted, and a support tool. The proposed approach uses case-based reasoning to retrieve previous cases similar to the current case. This approach also uses association rules to analyze patterns in the dataset and calculate the probability of risks associated with change requests. We prepared a case study to validate the proposal by analyzing the most frequent challenges in change management and considering how it can solve or minimize such problems. Results show that the proposed approach successfully assists decision-making, predicts potential risks, and suggests coherent solutions to the user. We have developed a support tool to evaluate this approach in practice with experts and obtained four different outcomes. Only a small set of cases failed to provide relevant results to the user. The use of case-based reasoning and association rules has proven to be advantageous in change management despite validity threats associated with the small number of test cases and experts involved