CADIAG: Approaches to computer-assisted medical diagnosis

Adlassnig, Klaus-Peter; Kolarz, G; Scheithauer, Werner; Effenberger, H.; Grabner, G

doi:10.1016/0010-4825(85)90014-9

Cited by 79 publications

(38 citation statements)

References 13 publications

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“…Let A denote the matrix of linear coefficients in (2). At every step of the simplex algorithm, A is represented as a combination (B, N ) where B and N are the submatrices of the basic and non-basic variables, respectively.…”

Section: Algorithmsmentioning

confidence: 99%

“…If the expression is satisfiable, it means that the column corresponds to a possible world (W in our context) and can be added to (2). Otherwise, the justifications of unsatisfiability (see [15]) are converted into linear constraints and added to the pricing-out problem, which is then re-optimized.…”

Section: Algorithmsmentioning

confidence: 99%

“…Its design and construction was initiated in the early 80's at the Medical University of Vienna by K.P. Adlassnig -see [1], [2], [3] or [4] for more on the origins and design of CADIAG-2.…”

Section: Introductionmentioning

confidence: 99%

“…The value η is intended to quantify the degree to which P (the antecedent) confirms Q (the consequent) and claimed in most of the literature on CADIAG-2 (see, for example, [2] or [3]) to have been calculated from a certain database or interpretation I as follows:…”

mentioning

confidence: 99%

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The Consistency of the CADIAG-2 Knowledge Base: A Probabilistic Approach

Klinov

Parsia

Parra³

2010

Logic for Programming, Artificial Intelligence, and Reasoning

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Abstract. The paper presents the methodology and the results of checking consistency of the knowledge base of CADIAG-2, a large-scale medical expert system. Such knowledge base consists of a large collection of rules representing knowledge about various medical entities (symptoms, signs, diseases...) and relationships between them. The major portion of the rules are uncertain, i.e., they specify to what degree a medical entity is confirmed by another medical entity or a combination of them. Given the size of the system and the uncertainty it has been challenging to validate its consistency. Recent attempts to partially formalise CADIAG-2's knowledge base into decidable Gödel logics have shown that, on that formalisation, CADIAG-2 is inconsistent. In this paper we verify this result with an alternative, more expressive formalisation of CADIAG-2 as a set of probabilistic conditional statements and apply a state-of-the-art probabilistic logic solver to determine satisfiability of the knowledge base and to extract conflicting sets of rules. As CADIAG-2 is too large to be handled out of the box we describe an approach to split the knowledge base into fragments that can be tested independently and prove that such methodology is complete (i.e., is guaranteed to find all conflicts). With this approach we are able to determine that CADIAG-2 contains numerous sets of conflicting rules and compute all of them for a slightly relaxed version of the knowledge base.

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Section: Algorithmsmentioning

confidence: 99%

Section: Algorithmsmentioning

confidence: 99%

“…Its design and construction was initiated in the early 80's at the Medical University of Vienna by K.P. Adlassnig -see [1], [2], [3] or [4] for more on the origins and design of CADIAG-2.…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

The Consistency of the CADIAG-2 Knowledge Base: A Probabilistic Approach

Klinov

Parsia

Parra³

2010

Logic for Programming, Artificial Intelligence, and Reasoning

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“…This approach may help in discovering inaccuracies in the knowledge base, and thus support the process of building a reliable medical expert system. Such consistency tests were carried out for the logical reformulation of the HEPAR system, a rule-based expert system for the diagnosis of disorders of the liver and biliary tract [Lucas & Janssens, 1991], and the CADIAG-1/BIN system, a medical expert system in the broad domain of internal medicine [Adlassnig et al, 1985].…”

Section: Heuristic Knowledgementioning

confidence: 99%

Logic engineering in medicine

Lucas¹

1995

The Knowledge Engineering Review

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The safety-critical nature of the application of knowledge-based systems to the field of medicine, demands 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 offer such a foundation, thus promoting scrutinous 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 different 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.

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RENOIR: An expert system using fuzzy logic for rheumatology diagnosis

Serrano¹,

Sierra²,

Mántaras³

1994

Int. J. Intell. Syst.

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A new expert system (ES) to aid the nonspecialist physician in diagnosing arthritis and collagen diseases has been developed. Here we present the structure of RENOIR and the results of its implementation. This rule-based ES has been programmed using the MILORD environment. This is a shell to develop ES using a closed set of linguistic labels to express uncertainty. A feature of RENOIR is its five levels of knowledge representation, which permits to build a very flexible knowledge base (KB) and express knowledge with high accuracy. Those rules directed to similar goals are grouped in modules to improve computational performance and for higher clarity of the KB. Control of the reasoning process is assured by several mechanisms, one of the main being metarules specifically designed for almost all the knowledge levels of the KB. We have used public domain knowledge (books, criteria tables) and personal heuristics from one of the authors (Belmonte-Serrano) to implement the KB of RENOIR. In its present form, our KB comprises 1 058 rules, 978 facts, 220 metarules, and 34 modules. A first validation process has shown good performance of the ES compared to 12 physicians with diverse levels of experience in rheumatic diseases. New ongoing versions of the system with improved interfaces,and reasoning capabilities are expected before verifying RENOIR's clinical acceptability.

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CADIAG: Approaches to computer-assisted medical diagnosis

Cited by 79 publications

References 13 publications

The Consistency of the CADIAG-2 Knowledge Base: A Probabilistic Approach

The Consistency of the CADIAG-2 Knowledge Base: A Probabilistic Approach

Logic engineering in medicine

RENOIR: An expert system using fuzzy logic for rheumatology diagnosis

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