Medical diagnosis: Are expert systems needed?

Bouckaert, A.

doi:10.1016/0020-7101(87)90020-1

Cited by 10 publications

(6 citation statements)

References 15 publications

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“…These methods must be distinguished from decision-support programs based on statistical procedures. 27 - 29 We consider statistically based meth-ods, especially Bayesian techniques, 30 -31 to be inadequate tools for a topologic diagnosis-support system; the set of topologic hypotheses is generally not exhaustive and mutually exclusive as assumed by Bayesian statistics. 29 -32 Thus, the frequent involvement of two or more cerebral territories by a stroke contradicts the assumption of only one correct diagnosis in each case as required by most statistical procedures.…”

Section: Resultsmentioning

confidence: 99%

The TOPOSCOUT expert system for stroke localization.

Spitzer

Thie

Caplan

et al. 1989

Stroke

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Clinically, strokes are localized by the findings on neurologic examination, TOPOSCOUT is an expert system designed to diagnose the anatomic location and the corresponding vascular territory of strokes based on the clinical signs and symptoms. The inference engine of TOPOSCOUT uses a backtracking algorithm and a rule-based data base that includes associations of neurologic signs with vascular and anatomic areas, TOPOSCOUT is capable of detecting typical stroke patterns, for example, "top-of-the-basilar" or Wallenberg's syndromes. The accuracy of TOPOSCOUT'S diagnoses has been tested for conformity with the final diagnoses of 129 patients in the Hamburg Stroke Data Bank, and a high level of agreement was found for hemispheric lesions. The program runs on microcomputers with MS-DOS and is intended as a practical aid for physicians not fully familiar with topologic stroke diagnosis and as an interactive teaching device. (Stroke 1989;20:1195-1201

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Section: Resultsmentioning

confidence: 99%

The TOPOSCOUT expert system for stroke localization.

Spitzer

Thie

Caplan

et al. 1989

Stroke

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“…In general, the validity of a system for differential diagnosis depends in particular upon the size, quality and completeness of the knowledge base [12], [13], [14], the organization of which directs possible inference mechanisms. Most knowledge-based systems use declarative rule-oriented programming with PROLOG or LISP to represent and process medical knowledge.…”

Section: Organization Of the Knowledge Basementioning

confidence: 99%

Development of a Computer-Aided Reference System for Differential Diagnostics Support

Wieding¹,

Kretschmar²,

Schönle³

1990

Methods Inf Med

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In a situation of uncertain diagnosis, physicians may spend valuable time consulting relevant literature, often with unsatisfactory results. Therefore, our aim was to develop a computer system which supports differential diagnostics via rapid and comprehensive searches through information in literature. Based on entered signs and symptoms our prototype is able to offer probable diagnoses. Subsequently, further examinations and tests are suggested to confirm or exclude a certain disease. Thus, the final diagnosis is made gradually by differentiation of possible diseases.Our first attempt consisted in representing knowledge in a rule-based PROLOG system. However, because nearly all information for a sign-oriented differential diagnosis can be represented with very few relations only, we turned to a fact-oriented representation of signs and diseases. Access was possible via PROLOG or an imperative programming language. The index-sequential access on a fact-oriented representation of knowledge was suitable to manage a large knowledge base, which is necessary for a thorough differential diagnosis. A pointer structure was recently examined in order to handle different object-to-object relations. Efficient information processing is now possible which provides short response times using even broad knowledge bases.

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“…Present Illness Program, PiP (Pauker et al [33]), Acid-Base Electrolytes, ABEL (Paril et al [34]), and INTERNIST-1 (Miller et al. [35]) are hypothesis-based systems (Bouckaert [36]). Many medical problems, especially those related only to laboratory data, can be solved using conventional algorithms as they contain data of known imprecision.…”

Section: Introductionmentioning

confidence: 99%

Use of artificial intelligence in analytical systems for the clinical laboratory

Place

Truchaud²,

Ozawa³

et al. 1995

Clinical Biochemistry

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Glostrup/Copehagen, Denmark. segmented flow system started with the auto-analysers of Skeggs [2], that were made commercially available at the end of the 1950s. This process has continued through many phases with the most recent advances being in the area of robotics [3]. Mechanization figure) and the open or stand-alone system (total figure). Metarules are part of the procedural knowledge of expert systems. They are used to evoke programs and to guide the inference process and thus define how to use the rules in the system. They are basic blocks of (usually) IF_THEN rules for exhaustive mapping.The inference engine may make use of one or a number of these problem-solving mechanisms. Problems with expert systemsSince the expert system is built using knowledge from the expert, it may perform more or less like an expert.However, as an expert system does not get tired, it will give more consistent results than its human counterparts. A good expert system may even give better results than human experts performing at their best.

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Medical diagnosis: Are expert systems needed?

Cited by 10 publications

References 15 publications

The TOPOSCOUT expert system for stroke localization.

The TOPOSCOUT expert system for stroke localization.

Development of a Computer-Aided Reference System for Differential Diagnostics Support

Use of artificial intelligence in analytical systems for the clinical laboratory

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