Medical Informatics: Searching for Underlying Components

2002 ◽  
Vol 41 (01) ◽  
pp. 12-19 ◽  
Author(s):  
M. A. Musen
Keyword(s):  
Problem Solving ◽  
Computer Science ◽  
Medical Informatics ◽  
Theoretical Basis ◽  
Basic Research ◽  
Knowledge Based Systems ◽  
The Core ◽  
Knowledge Based ◽  
Domain Ontologies ◽  
Unifying Principles

Abstract Objective: To discuss unifying principles that can provide a theory for the diverse aspects of work in medical informatics. If medical informatics is to have academic credibility, it must articulate a clear theory that is distinct from that of computer science or of other related areas of study. Results: The notions of reusable domain ontologies and problem-solving methods provide the foundation for current work on second-generation knowledge-based systems. These abstractions are also attractive for defining the core contributions of basic research in informatics. We can understand many central activities within informatics in terms defining, refining, applying, and evaluating domain ontologies and problem-solving methods. Conclusion: Construing work in medical informatics in terms of actions involving ontologies and problem-solving methods may move us closer to a theoretical basis for our field.

Scalable Software Architectures for Decision Support

1999 ◽  
Vol 38 (04/05) ◽  
pp. 229-238 ◽  
Author(s):  
M. A. Musen
Keyword(s):  
Decision Support ◽  
Problem Solving ◽  
Decision Support Systems ◽  
Medical Informatics ◽  
Intelligent Systems ◽  
Clinical Medicine ◽  
Support Systems ◽  
Basic Research ◽  
Building Blocks ◽  
Domain Ontologies

AbstractInterest in decision-support programs for clinical medicine soared in the 1970s. Since that time, workers in medical informatics have been particularly attracted to rule-based systems as a means of providing clinical decision support. Although developers have built many successful applications using production rules, they also have discovered that creation and maintenance of large rule bases is quite problematic. In the 1980s, several groups of investigators began to explore alternative programming abstractions that can be used to build decision-support systems. As a result, the notions of “generic tasks” and of reusable problem-solving methods became extremely influential. By the 1990s, academic centers were experimenting with architectures for intelligent systems based on two classes of reusable components: (1) problem-solving methods – domain-independent algorithms for automating stereotypical tasks – and (2) domain ontologies that captured the essential concepts (and relationships among those concepts) in particular application areas. This paper highlights how developers can construct large, maintainable decision-support systems using these kinds of building blocks. The creation of domain ontologies and problem-solving methods is the fundamental end product of basic research in medical informatics. Consequently, these concepts need more attention by our scientific community.

OPGEN: A System to Identify and Evaluate Potential Applications of Knowledge Based Systems

1992 ◽  
Author(s):  
Samir Rohatgi ◽  
James H. Oliver ◽  
Stuart S. Chen
Keyword(s):  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Knowledge Based Systems ◽  
The Core ◽  
Knowledge Based ◽  
Evaluation Functions ◽  
Potential Applications ◽  
Computer Based ◽  
Suitable System ◽  
Overall Effectiveness

Abstract This paper describes the development of OPGEN (Opportunity Generator), a computer based system to help identify areas where a knowledge based system (KBS) might be beneficial, and to evaluate whether a suitable system could be developed in that area. The core of the system is a knowledge base used to carry out the identification and evaluation functions. Ancillary functions serve to introduce and demonstrate KBS technology to enhance the overall effectiveness of the system. All aspects of the development, from knowledge acquisition through to testing are presented in this paper.

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.

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.

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.

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.

Involvement in Medical Informatics May Enable Pharmacists to Expand Their Consultation Potential and Improve the Quality of Healthcare

1992 ◽  
Vol 26 (1) ◽  
pp. 100-104 ◽  
Author(s):  
Alise E. Woodruff ◽  
C. Anthony Hunt
Keyword(s):  
Quality Assurance ◽  
Information Systems ◽  
Information Management ◽  
Medical Informatics ◽  
Quality Of Healthcare ◽  
Knowledge Based Systems ◽  
Related Services ◽  
Medical Computing ◽  
Knowledge Based

The outlook for pharmacy-related services foretells more involvement of both computers and information systems. Expert therapeutic systems and databases will enable pharmacists to expand their consultation potential through networks and improve the quality of healthcare that they provide. Therapeutic information management could be the largest pharmacy speciality of the future. As knowledge-based systems and networks become commonplace, there will be an increasing need for new components, system monitoring, and quality assurance. This is an opportunity for pharmacy to bring medical computing, as it relates to therapeutics, into the mainstream of the profession as a new discipline.

Trends in temporal representation and reasoning

1996 ◽  
Vol 11 (3) ◽  
pp. 281-288 ◽  
Author(s):  
Luca Chittaro ◽  
Angelo Montanari
Keyword(s):  
Computer Science ◽  
Knowledge Based Systems ◽  
Logical Foundations ◽  
The Third ◽  
Knowledge Based ◽  
Temporal Representation ◽  
Key West ◽  
Research Areas ◽  
Meaningful Connections ◽  
Or Applications

Time is one of the most relevant topics in AI. It plays a major role in several of AI research areas, ranging from logical foundations to applications of knowledge-based systems. Despite the ubiquity of time in AI, researchers tend to specialise and focus on time in particular contexts or applications, overlooking meaningful connections between different areas. In an attempt to promote crossfertilisation and reduce isolation, the Temporal Representation and Reasoning (TIME) workshop series was started in 1994. The third edition of the workshop was held on May 19–20 1996 in Key West, FL, with S. D. Goodwin and H. J. Hamilton as General Chairs, and L. Chittaro and A. Montanari as Program Chairs. A particular emphasis was given to the foundational aspects of temporal representation and reasoning through an investigation of the relationships between different approaches to temporal issues in AI, computer science and logic.

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