Reconstructing Medical Problem Solving Competence: MACCORD

1993 ◽  
Vol 32 (04) ◽  
pp. 326-338
Author(s):  
B. Petkoff ◽  
H. Mannebach ◽  
S. Kirkby ◽  
D. Kraus
Keyword(s):  
Problem Solving ◽  
Clinical Medicine ◽  
Medical Knowledge ◽  
Medical Problem ◽  
Knowledge Based Systems ◽  
Levels Of Abstraction ◽  
Knowledge Based ◽  
Methodological Principles ◽  
Problem Solving Behavior ◽  
Medical Reasoning

AbstractThe building of medical knowledge-based systems involves the reconstruction of methodological principles and structures within the various subdomains of medicine. ACCORD is a general methodology of knowledge-based systems, and MACCORD its application to medicine. MACCORD represents the problem solving behavior of the medical expert in terms of various types of medical reasoning and at various levels of abstraction. With MACCORD the epistemic and cognitive processes in clinical medicine can be described in formal terminology, covering the entire diversity of medical reasoning. MACCORD is close enough to formalization to make a significant contribution to the fields of medical knowledge acquisition, medical didactics and the analysis and application of medical problem solving methods.

scholarly journals Logic engineering in medicine

1995 ◽  
Vol 10 (2) ◽  
pp. 153-179 ◽  
Author(s):  
Peter J. F. Lucas
Keyword(s):  
Problem Solving ◽  
Medical Knowledge ◽  
Medical Problem ◽  
Small Scale ◽  
Knowledge Based Systems ◽  
Types Of Knowledge ◽  
Knowledge Based ◽  
Powerful Means ◽  
Solid Foundation ◽  
Insight Into

AbstractThe safety-critical nature of the application of knowledge-based systems to the field of medicine requires the adoption of reliable engineering principles with a solid foundation for their construction. Logical languages with their inherent, precise notions of consistency, soundness and completeness provide such a foundation, thus promoting scrupulous engineering of medical knowledge. Moreover, logic techniques provide a powerful means for getting insight into the structure and meaning of medical knowledge used in medical problem solving. Unfortunately, logic is currently only used on a small scale for building practical medical knowledge-based systems. In this paper, the various approaches proposed in the literature are reviewed, and related to the various types of knowledge and problem solving employed in the medical field. The appropriateness of logic for building medical knowledge-based expert systems is further motivated.

Task Ontology-Based Human-Computer Interaction

2009 ◽  
pp. 950-960
Author(s):  
Kazuhisa Seta
Keyword(s):  
Problem Solving ◽  
Real World ◽  
Practical Knowledge ◽  
Building Blocks ◽  
Research Field ◽  
Knowledge Based Systems ◽  
Engineering Research ◽  
Knowledge Based ◽  
Task Ontology ◽  
Problem Solving Task

In ontological engineering research field, the concept of “task ontology” is well-known as a useful technology to systemize and accumulate the knowledge to perform problem-solving tasks (e.g., diagnosis, design, scheduling, and so on). A task ontology refers to a system of a vocabulary/ concepts used as building blocks to perform a problem-solving task in a machine readable manner, so that the system and humans can collaboratively solve a problem based on it. The concept of task ontology was proposed by Mizoguchi (Mizoguchi, Tijerino, & Ikeda, 1992, 1995) and its validity is substantiated by development of many practical knowledge-based systems (Hori & Yoshida, 1998; Ikeda, Seta, & Mizoguchi, 1997; Izumi &Yamaguchi, 2002; Schreiber et al., 2000; Seta, Ikeda, Kakusho, & Mizoguchi, 1997). He stated: …task ontology characterizes the computational architecture of a knowledge-based system which performs a task. The idea of task ontology which serves as a system of the vocabulary/concepts used as building blocks for knowledge-based systems might provide an effective methodology and vocabulary for both analyzing and synthesizing knowledge-based systems. It is useful for describing inherent problem-solving structure of the existing tasks domain-independently. It is obtained by analyzing task structures of real world problem. ... The ultimate goal of task ontology research is to provide a theory of all the vocabulary/concepts necessary for building a model of human problem solving processes. (Mizoguchi, 2003) We can also recognize task ontology as a static user model (Seta et al., 1997), which captures the meaning of problem-solving processes, that is, the input/output relation of each activity in a problem-solving task and its effects on the real world as well as on the humans’ mind.

Task Ontology-Based Human-Computer Interaction

2006 ◽  
pp. 588-596
Author(s):  
Kazuhisa Seta
Keyword(s):  
Problem Solving ◽  
Real World ◽  
Practical Knowledge ◽  
Building Blocks ◽  
Research Field ◽  
Knowledge Based Systems ◽  
Engineering Research ◽  
Knowledge Based ◽  
Task Ontology ◽  
Problem Solving Task

In ontological engineering research field, the concept of “task ontology” is well-known as a useful technology to systemize and accumulate the knowledge to perform problem-solving tasks (e.g., diagnosis, design, scheduling, and so on). A task ontology refers to a system of a vocabulary/concepts used as building blocks to perform a problem-solving task in a machine readable manner, so that the system and humans can collaboratively solve a problem based on it. The concept of task ontology was proposed by Mizoguchi (Mizoguchi, Tijerino, & Ikeda, 1992, 1995) and its validity is substantiated by development of many practical knowledge-based systems (Hori & Yoshida, 1998; Ikeda, Seta, & Mizoguchi, 1997; Izumi &Yamaguchi, 2002; Schreiber et al., 2000; Seta, Ikeda, Kakusho, & Mizoguchi, 1997). He stated: …task ontology characterizes the computational architecture of a knowledge-based system which performs a task. The idea of task ontology which serves as a system of the vocabulary/concepts used as building blocks for knowledge-based systems might provide an effective methodology and vocabulary for both analyzing and synthesizing knowledge-based systems. It is useful for describing inherent problem-solving structure of the existing tasks domain-independently. It is obtained by analyzing task structures of real world problem. ... The ultimate goal of task ontology research is to provide a theory of all the vocabulary/concepts necessary for building a model of human problem solving processes. (Mizoguchi, 2003) We can also recognize task ontology as a static user model (Seta et al., 1997), which captures the meaning of problem-solving processes, that is, the input/output relation of each activity in a problem-solving task and its effects on the real world as well as on the humans’ mind.

Altering the SightPlan knowledge-based systems

1992 ◽  
Vol 6 (1) ◽  
pp. 19-37 ◽  
Author(s):  
I. D. Tommelein ◽  
B. Hayes-Roth ◽  
R. E. Levitt
Keyword(s):  
Problem Solving ◽  
Knowledge Bases ◽  
Knowledge Based Systems ◽  
Knowledge Domain ◽  
Task Knowledge ◽  
Knowledge Based ◽  
Site Layout ◽  
Different Types ◽  
Control Knowledge ◽  
Construction Site Layout

SightPlan refers to several knowledge-based systems that address construction site layout. Five different versions were implemented and their components of expertise are described here. These systems are alterations of one another, differing either in the problems they solve, the problem-solving methods they apply, or the tasks they address. Because they share either control knowledge, domain concepts, or heuristics, and such knowledge is implemented in well-defined modular knowledge bases, these systems could easily re-use parts of one another. Experiments like those presented here may clarify the role played by different types of knowledge during problem solving, enabling researchers to gain a broader understanding of the generality of the domain and task knowledge that is embedded in KBSs and of the power of their systems.

An epistemological framework for medical knowledge-based systems

1992 ◽  
Vol 22 (6) ◽  
pp. 1361-1375 ◽  
Author(s):  
M. Ramoni ◽  
M. Stefanelli ◽  
L. Magnani ◽  
G. Barosi
Keyword(s):  
Medical Knowledge ◽  
Knowledge Based Systems ◽  
Knowledge Based ◽  
Epistemological Framework

Explaining reasoning: an overview of explanation in knowledge-based systems

1991 ◽  
Vol 6 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Richard W. Southwick
Keyword(s):  
Problem Solving ◽  
Annotated Bibliography ◽  
User Requirements ◽  
Knowledge Based Systems ◽  
Reasoning Process ◽  
Knowledge Based ◽  
Problem Solving Process ◽  
System Context

AbstractThere seems to be general agreement amongst those involved in KBS research that in order to be useful, a system must be able to explain its reasoning to a user. This paper reviews the development of explanation facilities in knowledge-based systems. It differentiates between explanation as a problem-solving process, and that which explains a reasoning process. This review concentrates on the latter, identifying and giving examples of three categories of reasoning explanation.We then look at user requirements for explanation. What makes an explanation useful depends on the expectations of a user, which in turn depends on such issues as user background and system context. Several techniques are examined that have been applied to the problem of producing explanations that are appropriately structured and conveyed.Finally, we discuss some of the work that has been done in describing theories of human discourse and explanation, and some issues that will become increasingly important for future explanation systems.An extensive annotated bibliography is provided.

scholarly journals Enabling the use of hereditary information from pedigree tools in medical knowledge-based systems

2013 ◽  
Vol 46 (4) ◽  
pp. 710-720 ◽  
Author(s):  
Pablo Gay ◽  
Beatriz López ◽  
Albert Plà ◽  
Jordi Saperas ◽  
Carles Pous
Keyword(s):  
Medical Knowledge ◽  
Knowledge Based Systems ◽  
Knowledge Based
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