Knowledge-light adaptation approaches in case-based reasoning for radiotherapy treatment planning

Objective: Radiotherapy treatment planning aims at delivering a sufficient radiation dose to cancerous tumour cells while sparing healthy organs in the tumour-surrounding area. It is a time-consuming trial-and-error process that requires the expertise of a group of medical experts including oncologists and medical physicists and can take from 2 to 3 h to a few days. Our objective is to improve the performance of our previously built case-based reasoning (CBR) system for brain tumour radiotherapy treatment planning. In this system, a treatment plan for a new patient is retrieved from a case base containing patient cases treated in the past and their treatment plans. However, this system does not perform any adaptation, which is needed to account for any difference between the new and retrieved cases. Generally, the adaptation phase is considered to be intrinsically knowledge-intensive and domain-dependent. Therefore, an adaptation often requires a large amount of domain-specific knowledge, which can be difficult to acquire and often is not readily available. In this study, we investigate approaches to adaptation that do not require much domain knowledge, referred to as knowledge-light adaptation. Methodology: We developed two adaptation approaches: adaptation based on machine-learning tools and adaptation-guided retrieval. They were used to adapt the beam number and beam angles suggested in the retrieved case. Two machine-learning tools, neural networks and naive Bayes classifier, were used in the adaptation to learn how the difference in attribute values between the retrieved and new cases affects the output of these two cases. The adaptation-guided retrieval takes into consideration not only the similarity between the new and retrieved cases, but also how to adapt the retrieved case. Results: The research was carried out in collaboration with medical physicists at the Nottingham University Hospitals NHS Trust, City Hospital Campus, UK. All experiments were performed using real-world brain cancer patient cases treated with three-dimensional (3D)-conformal radiotherapy. Neural networks-based adaptation improved the success rate of the CBR system with no adaptation by 12%. However, naive Bayes classifier did not improve the current retrieval results as it did not consider the interplay among attributes. The adaptation-guided retrieval of the case for beam number improved the success rate of the CBR system by 29%. However, it did not demonstrate good performance for the beam angle adaptation. Its success rate was 29% versus 39% when no adaptation was performed. Conclusions: The obtained empirical results demonstrate that the proposed adaptation methods improve the performance of the existing CBR system in recommending the number of beams to use. However, we also conclude that to be effective, the proposed adaptation of beam angles requires a large number of relevant cases in the case base

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

Audiometric Decision Support System

Audiology is one of a medical field which study of hearing and hearing disorders for individuals who have hearing loss. Clinician as a user has proposed one decision support system named Audiometric System. This audiology service is focusing only on industry that handles a business process with machines. Usually, this kind of industry environment is exposing to the noise which can affect employees' hearing. The objectives of this project are to help clinician reduce time for retrieving appropriate medical history in order to solve hearing problem, to assist clinicians make a correct decision byproviding an accurate result from the manipulation of baseline audiogram and annual audiogram, andto provide a prototype for the audiology service. The problem statements of this project are time-consuming to solve the hearing problem and difficult to provide an accurate result from the baseline audiogram and annual audiogram. The scope of study is mainly covers on the clinician that have responsibility on clinical management. Clinicians such as doctor or nurse use the audiometric system as an audiology service to solve the hearing problem. Methodology used in this project is known as Waterfall model which includes information gathering or analysis phase, design phase, development phase, testing phase and evaluation phase. As a conclusion, a successful implementation of Audiometric System as one of audiology service hopefully is able to help clinicians improve the clinical performance especially in solving the occupational hearing loss of employees. Aprototype of Audiometric System is produced through this project

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

CWDM: A Case-based Diabetes Management Web System

Managing diabetes using intelligent techniques is a recent priority for healthcare information systems and the medical domain. Diabetes is one of the most widespread diseases around the world including Australia. Numerous intelligent systems supporting diabetes management (DM) have been widely deployed, yet how to effectively develop a DM system integrating intelligent techniques remains a big issue. Case-based reasoning (CBR), as an intelligent technique, has been applied in various fields including customer services, medical diagnosis, and clinical treatment. This paper proposes a case-based lifecycle for DM consisting of case-based symptoms, case-based diagnosis, case-based prognosis, case-based treatment, and case-based care. The lifecycle is integrated with a web-based system in which CBR functions as an intelligent intermediary. The approach proposed in this research might facilitate research and development of diabetes management, healthcare information systems and intelligent systems

Improving Retrieval Performance of Case Based Reasoning Systems by Fuzzy Clustering

Case-based reasoning (CBR), which is a classical reasoning methodology, has been put to use. Its application has allowed significant progress in resolving problems related to the diagnosis, therapy, and prediction of diseases. However, this methodology has shown some complicated problems that must be resolved, including determining a representation form for the case (complexity, uncertainty, and vagueness of medical information), preventing the case base from the infinite growth of generated medical information and selecting the best retrieval technique. These limitations have pushed researchers to think about other ways of solving problems, and we are recently witnessing the integration of CBR with other techniques such as data mining. In this article, we develop a new approach integrating clustering (Fuzzy C-Means (FCM) and K-Means) in the CBR cycle. Clustering is one of the crucial challenges and has been successfully used in many areas to develop innate structures and hidden patterns for data grouping [1]. The objective of the proposed approach is to solve the limitations of CBR and improve it, particularly in the search for similar cases (retrieval step). The approach is tested with the publicly available immunotherapy dataset. The results of the experimentations show that the integration of the FCM algorithm in the retrieval step reduces the search space (the large volume of information), resolves the problem of the vagueness of medical information, speeds up the calculation and response time, and increases the search efficiency, which further improves the performance of the retrieval step and, consequently, the CBR system

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

Knowledge formalization in experience feedback processes : an ontology-based approach

Because of the current trend of integration and interoperability of industrial systems, their size and complexity continue to grow making it more difficult to analyze, to understand and to solve the problems that happen in their organizations. Continuous improvement methodologies are powerful tools in order to understand and to solve problems, to control the effects of changes and finally to capitalize knowledge about changes and improvements. These tools involve suitably represent knowledge relating to the concerned system. Consequently, knowledge management (KM) is an increasingly important source of competitive advantage for organizations. Particularly, the capitalization and sharing of knowledge resulting from experience feedback are elements which play an essential role in the continuous improvement of industrial activities. In this paper, the contribution deals with semantic interoperability and relates to the structuring and the formalization of an experience feedback (EF) process aiming at transforming information or understanding gained by experience into explicit knowledge. The reuse of such knowledge has proved to have significant impact on achieving themissions of companies. However, the means of describing the knowledge objects of an experience generally remain informal. Based on an experience feedback process model and conceptual graphs, this paper takes domain ontology as a framework for the clarification of explicit knowledge and know-how, the aim of which is to get lessons learned descriptions that are significant, correct and applicable

Intelligent Knowledge Acquisition with Case-Based Reasoning Techniques

Knowledge management systems are an emerging area gaining interest in organisations. This paper discusses the application of case based reasoning techniques and intelligent agents in the knowledge acquisition phase of knowledge management systems so that an intelligent knowledge acquisition process is possible

Diabetes Diagnosis by Case-Based Reasoning and Fuzzy Logic

In the medical field, experts’ knowledge is based on experience, theoretical knowledge and rules. Case-based reasoning is a problem-solving paradigm which is based on past experiences. For this purpose, a large number of decision support applications based on CBR have been developed. Cases retrieval is often considered as the most important step of case-based reasoning. In this article, we integrate fuzzy logic and data mining to improve the response time and the accuracy of the retrieval of similar cases. The proposed Fuzzy CBR is composed of two complementary parts; the part of classification by fuzzy decision tree realized by Fispro and the part of case-based reasoning realized by the platform JColibri. The use of fuzzy logic aims to reduce the complexity of calculating the degree of similarity that can exist between diabetic patients who require different monitoring plans. The results of the proposed approach are compared with earlier methods using accuracy as metrics. The experimental results indicate that the fuzzy decision tree is very effective in improving the accuracy for diabetes classification and hence improving the retrieval step of CBR reasoning