author={Peter Funk and Ning Xiong},
  journal={Computational Intelligence},
  • P. FunkN. Xiong
  • Published 1 August 2006
  • Computer Science
  • Computational Intelligence
This paper discusses the role and integration of knowledge discovery (KD) in case‐based reasoning (CBR) systems. The general view is that KD is complementary to the task of knowledge retaining and it can be treated as a separate process outside the traditional CBR cycle. Unlike knowledge retaining that is mostly related to case‐specific experience, KD aims at the elicitation of new knowledge that is more general and valuable for improving the different CBR substeps. KD for CBR is exemplified by… 

Case-Based Reasoning in the Health Sciences: Foundations and Research Directions

This chapter begins with introductions to the CBR approach and to health sciences domains, and then explains their synergistic combination, and presents an in-depth analysis of current work in the field, classifying CBR in the Health Sciences systems in terms of their domains, purposes, memory and case management, reasoning, and system design.

Special issue on case-based reasoning in the health sciences

Nine high‐quality papers introduced here represent the research and experience of twenty‐two authors working in eight different countries on a wide range of problems and projects and illustrate some of the major trends of current research in CBR in the health sciences.


Nine high-quality papers introduced here represent the research and experience of twenty-two authors working in eight different countries on a wide range of problems and projects and illustrate some of the major trends of current research in CBR in the health sciences.

Synergistic case-based reasoning in medical domains

Data mining and CBR integrated methods in medicine: a review

This review provides a comprehensive view of the state of the art of single DM and integrated case-based reasoning (CBR) and DM techniques in different medical domains such as: general medicine, nephrology, dermatology, cardiology, urology, oncology, neurology and orthopaedic.

Case-Based Reasoning Systems in the Health Sciences: A Survey of Recent Trends and Developments

A survey of recent medical CBR systems based on a literature review and an e-mail questionnaire sent to the corresponding authors of the papers where these systems are presented suggests some clear trends have been identified, such as multipurpose systems.

Case-based systems in health sciences: a case study in the field of stress

This paper investigates the state-of-the art of case-based reasoning (CBR), a recent AI method in the medical domain and proposes a solution analyzing the relation between stress and finger temperature using case-Based reasoning and other AI techniques namely case- based reasoning, textual CBR, rule-based Reasoning, and fuzzy logic to support classification and diagnosis in stress management.

Knowledge Discovery in the Form of Prototypical Cases Using Advanced Data Mining Techniques

This thesis applies advanced data mining techniques to obtain structured and abstract knowledge structures (or prototypical cases) from clinical data available in a certain medical domain to investigate if advanced knowledge structures in the form of prototypical Case-Based Reasoning can be generated from clinical Data Mining techniques.

A linked data-based approach for clinical treatment selecting support

A linked data-based approach for treatment plan selection is proposed that integrates the patients' clinical records in hospitals with open linked data sources out of hospitals and reorganizes the electronic medical records of 97 colon cancer patients using the linked data model.



Intelligent Knowledge Retrieval for Decision Support in Medical Applications

A Multi Modal Reasoning methodology is defined, that integrates CBR and Rule Based Reasoning, for supporting context detection, information retrieval and therapy revision in diabetes care.

Case-Based Reasoning in CARE-PARTNER: Gathering Evidence for Evidence-Based Medical Practice

The role of case-based reasoning is presented in this paper as the collection of evidence for evidence-based medical practice and enhances the system by conferring an ability to learn from experience, and thus improve results over time.

Advancements and Trends in Medical Case-Based Reasoning: An Overview of Systems and System Development

System properties, divided into construction-oriented and purpose-oriented, are used as the basis for a survey of recent publications and research projects to find current trends in present medical CBR research.

Dynamic and static approaches to clinical data mining

Temporal Abstractions and Case-Based Reasoning for Medical Course Data: Two Prognostic Applications

The application of the method for analysis and prognosis of multiparametric kidney function courses, which combines two abstraction steps (state abstraction and temporal abstraction) with Case-based Reasoning with case-based reasoning, is presented.

Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches

An overview of the foundational issues related to case-based reasoning is given, some of the leading methodological approaches within the field are described, and the current state of the field is exemplified through pointers to some systems.

Case-Based Reasoning in the Care of Alzheimer's Disease Patients

The Auguste Project is an effort to provide decision support for planning the ongoing care of AD patients, using CBR and other thought processes natural to members of geriatric interdisciplinary teams, and the first system prototype has just been completed.

Fault diagnosis in industry using sensor readings and case-based reasoning

A novel method for diagnosis of faults by means of case-based reasoning and signal processing is proposed and has been applied to fault diagnosis of industrial robots at ABB Robotics and the results of experiments are very promising.

From Data Mining to Knowledge Discovery in Databases

An overview of this emerging field is provided, clarifying how data mining and knowledge discovery in databases are related both to each other and to related fields, such as machine learning, statistics, and databases.