Knowledge Acquisition for Expert Systems: Experience in Leukaemia Diagnosis

Fox, John; Myers, Christopher D.; Greaves, Melvyn F.; Pegram, Susan

doi:10.1055/s-0038-1635361

Cited by 29 publications

(8 citation statements)

References 6 publications

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“…Some famillar tasks do not Involve expllclt verbalizatlon of reasoning, but experts In any domain ,:an be asked to do this while conducting their famillar task. An example Is Fox, Myers, Greaves, & Pegram's (1985) study of lukemla diagnosis. In their procedure, oncologlsts were instructed to think out loud while performing a famillar diagnosis task,…”

Section: Analysis Of Familiar Tasksmentioning

confidence: 99%

“…The test cases are presented to the expert, who carries out their familiar task. For example , Fox, et al (1985) used test cases (i.e., data on patients) In their study of lukemia diagnosis. The test case method could also be called a "simulated familiar task" or "slmulated real time" In time-critical tasks, such as meteorological Forecasting (HoFfman, 1987).…”

Section: 1mentioning

confidence: 99%

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A survey of methods for eliciting the knowledge of experts

Hoffman

1989

SIGART Bull.

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A variety of methods have been used by expert system developers to elicit experts' knowledge and reasoning strategies. Additionally, a number of methods have been used by experimental psychologists to explore hypotheses about the nature of expertise. Methods can be placed into three broad categories: (1) Analyses of the tasks that experts perform, (2) Interview techniques, and (3) Special tasks. A comparison of knowledge elicitation methods yields recommendations about how expert system developers can deal with the "knowledge acquisition bottleneck."

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Section: Analysis Of Familiar Tasksmentioning

confidence: 99%

Section: 1mentioning

confidence: 99%

A survey of methods for eliciting the knowledge of experts

Hoffman

1989

SIGART Bull.

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show abstract

“…Several techniques are in use, some are more structured and systematic than others (Grover, 1983;Welbank, 1983;Bainbridge, 1985Bainbridge, , 1986Fox, Myers, Greaves & Pegram, 1985). The experts' time is limited and costly, as is that of those who build expert systems, it is therefore important to know which techniques lead to the best results for the time invested.…”

Section: Introductionmentioning

confidence: 99%

Comparing knowledge elicitation techniques: a case study

Schweickert¹,

Burton

Taylor

et al. 1987

Artif Intell Rev

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Three knowledge elicitation techniques were used to extract knowledge bases from experts on lighting for industrial inspection tasks. The techniques were: I a structured interview; II 'twenty questions'-imputing rules from information requests; and IIIa card sort. The first two techniques generate protocols, and in these cases two knowledge engineers independently extracted production rules from the protocols. In the third technique rules were derived from the classification of lighting solutions. The first two techniques led to about the same number of rules, the card sort to less. There was a slightly higher percentage of agreement between the rules extracted separately by the knowledge engineers for the normal interview than for the 'twenty questions' technique. Disagreements between the knowledge engineers were resolved by discussion, and an expert system to select special lights for inspection tasks was implemented. Of the rules finally agreed on, the percentage which could be implemented in the expert system was less for the twenty questions technique than for the others. The agreed rules were mailed to the expert, who indicated whether she agreed, disagreed, or wanted to make a modification. This secondary stage of elicitation revealed no evident difference between the three techniques in terms of the proportion of rules validated by the expert.

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“…The aim of knowledge acquisition is to obtain a body of knowledge which is as complete, consistent and correct as possible [14]. Most common methods of knowledge acquisition involve either eliciting information from human experts or extracting knowledge automatically from data.…”

Section: Acquisition Of Radiological Knowledgementioning

confidence: 99%

Elicitation and Representation of Expert Knowledge for Computer Aided Diagnosis in Mammography

Taylor¹,

Lee²,

Alberdi³

2004

Methods Inf Med

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This is the unspecified version of the paper.This version of the publication may differ from the final published version. We used these data to design a scheme with 50 descriptors. In Study 2, ten radiologists used the scheme to describe 40 sets of calcifications. We assessed the capacity of the terms to discriminate between benign and malignant calcifications, testing them against radiologists' assessments of malignancy and follow-up data. Permanent repository link Results:The descriptors that were found to be the most discriminating in Study 2 were included in CADIMUM II's knowledge base. They were represented as arguments for either a benign or a malignant diagnosis. These arguments are the central component of the decision support provided by the system. Other components are: image processing algorithms for the detection and measurement of calcifications and a set of rules that use the measures to decide which of the arguments apply to a given set of calcifications.. Conclusions:Preliminary evaluations of the CADMIUM II prototype reinforce the value of representing explicitly decision making processes in computer aided mammography and of deriving these processes from image processing measurements. Decision support is presented here at a level of description that is both relevant and meaningful to the user.

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Knowledge Acquisition for Expert Systems: Experience in Leukaemia Diagnosis

Cited by 29 publications

References 6 publications

A survey of methods for eliciting the knowledge of experts

A survey of methods for eliciting the knowledge of experts

Comparing knowledge elicitation techniques: a case study

Elicitation and Representation of Expert Knowledge for Computer Aided Diagnosis in Mammography

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