Qualitative representation of behavior in the medical domain

Bylander, Tom; Smith, Jack W.; Svirbely, John R.

doi:10.1016/0010-4809(88)90051-1

Cited by 15 publications

(6 citation statements)

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“…In addition to the core ontology (including the hierarchies of entities), additional process rules or heuristics must be added to produce a software system capable of inference for structure-based reasoning about processes. Evolved from expert systems, qualitative process modeling systems have allowed experimental biology and physical simulations to be created from ontologies and inferential process rules (Bylander et al, 1988;Trelease and Park, 1996;Trelease et al, 1999).…”

Section: Birth Of Ai and Symbolic Programmingmentioning

confidence: 97%

Anatomical reasoning in the informatics age: Principles, ontologies, and agendas

Trelease

2006

Anat. Rec.

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Reasoning about anatomy shares historical scientific roots with formal logic and artificial intelligence. With advances in computer-based intelligent programming, high-level biological structural knowledge may be exploited directly for biomedical research, clinical tasks, and educational applications. We consider the special nature of anatomical domain knowledge, emphasizing the complex concepts and semantics that must be represented in the development of ontologies, formally structured databases of biological information. We review the evolution of the fundamental scientific principles of logic and artificial intelligence needed for building machines that can make use of anatomical knowledge. We look at methods for compiling ontologies and compare the structural designs of the Foundational Model of Anatomy and Open GALEN ontologies. We further consider issues related to mapping developing anatomy resources with other biological ontologies in genomics, proteomics, and physiology. Although early results are promising, considerable resources and continuing effort must be committed to completing and extending anatomical ontologies for the ultimate success of computer-based anatomical reasoning. Anat Rec (Part B: New Anat) 289B:72-84, 2006. (c) 2006 Wiley-Liss, Inc.

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Section: Birth Of Ai and Symbolic Programmingmentioning

confidence: 97%

Anatomical reasoning in the informatics age: Principles, ontologies, and agendas

Trelease

2006

Anat. Rec.

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“…3 Functional modularity is an important consequence of this point of view. As we have shown in a number of papers (Bylander, Smith and Svirbely, 1986;Chandrasekaran, 1983Chandrasekaran, , 1986Chandrasekaran, Tanner and Josephson, 1988) this functional modularity makes system building and debugging easier, and the task-specific knowledge and control constructs help in knowledge acquisition and explanation. 4 The above list is not meant to be a complete list of generic tasks useful in knowledge-based problem solving.…”

Section: F Some Implications Ofgtsmentioning

confidence: 98%

“…For the diagnosis problem the criteria for forming classification hierarchies are discussed, with examples from the medical domain, by Bylander, Smith and Svirbely (1986), and in the engineering domain by Myers, Davis and Herman (1988). The hierarchy may mix function-subfunction and part-subpart views depending upon the way diagnostic reasoning actually works in the domain.…”

Section: Encoding the Hierarchy Of Malfunctionsmentioning

confidence: 99%

“…• Knowledge acquisition. Each generic task is associated with its own knowledge acquisition strategy for building an efficient problem solver (Bylander, Smith and Svirbely, 1986). For example in hierarchical classification, the knowledge engineer needs to find out what specific categories should be contained in the classification hierarchy and what general categories provide the most leverage for the establish-refine strategy.…”

Section: A Important Properties Of the Toolsetmentioning

confidence: 99%

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Generic tasks as building blocks for knowledge-based systems: the diagnosis and routine design examples

Chandrasekaran

1988

The Knowledge Engineering Review

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The level of abstraction of much of the work in knowledge-based systems (the rule, frame, logic level) is too low to provide a rich enough vocabulary for knowledge and control. I provide an overview of a framework called the Generic Task approach that proposes that knowledge systems should be built out of building blocks, each of which is appropriate for a basic type of problem solving. Each generic task uses forms of knowledge and control strategies that are characteristic to it, and are in general conceptually closer to domain knowledge. This facilitates knowledge acquisition and can produce a more perspicuous explanation of problem solving. The relationship of the constructs at the generic task level to the rule-frame level is analogous to that between high-level programming languages and assembly languages in computer science. I describe a set of generic tasks that have been found particularly useful in constructing diagnostic, design and planning systems. In particular, I describe two tools, CSRL and DSPL, that are useful for building classification-based diagnostic systems and skeletal planning systems respectively, and a high level toolbox that is under construction called the Generic Task toolbox.

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“…Another possibility is to describe in detail the functions and behaviors currently summarized by a relation type. This was done in a simplified model of the complement system, a small part of the host defense system of the human body (Sticklen and Chandrasekaran, 1989) and for modeling hypovolemia (Bylander et al, 1986). The functional reasoning of such a system integrated with the compiled associative knowledge will give a "deep model" according to Chandrasekaran's definition (Chandrasekaran et aI., 1987).…”

Section: Anatomical Structures and Movementsmentioning

confidence: 99%

Mesicar-a Medical Expert System Integrating Causal and Associative Reasoning

Horn

1989

Applied Artificial Intelligence

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A fundamental understanding of the anatomy of the human body is a prerequisite for the successful application of a rheumatological expert system designed for use in an outpatient setting. MESICAR incorporates extensive anatomical knowledge that provides the basis for the reasoning methods, which try to explain which structures may be the cause of visible problems of the patient. Associative relations between diseases and manifestations are combined with expressions that define conditions on the manifestations. These conditions are evaluated following the causal relations in the anatomical net. This paper concentrates on the associative and causal reasoning methods of MESICAR and their integration.

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Qualitative representation of behavior in the medical domain

Cited by 15 publications

References 4 publications

Anatomical reasoning in the informatics age: Principles, ontologies, and agendas

Anatomical reasoning in the informatics age: Principles, ontologies, and agendas

Generic tasks as building blocks for knowledge-based systems: the diagnosis and routine design examples

Mesicar-a Medical Expert System Integrating Causal and Associative Reasoning

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