The design and construction of a medical simulation model

Todd, Bryan S.; Stamper, R.; Macpherson, Paul

doi:10.1016/0169-2607(94)90044-2

Cited by 3 publications

(3 citation statements)

References 29 publications

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“…Hypotension is a cause of facial pallor. (The weight of this rule is different to the zero weight shown in [51)…”

Section: Resultsmentioning

confidence: 88%

“…Although we developed• this rule-ba~ed representation specifically for inferential knowledge, there is no formal requirement that we order the propositions in an inferential sequence. If, instead, we choose a causal sequence like we did for the Bayesian networks (but now the sequence contains atomic propositions rather than simple variables), then we have an efficient representation for causal knowledge [51] offering a considerable parameter reduction compared with Bayesian networks with explicit conditional probability tables. Although the "Noisy OR-Gate" is usually employed for this purpose, the logistic model has the advantage of being able to handle inhibitory influences as well as causal ones, and in any case it can usually approximate the "Noisy OR-Gate" when required [51].…”

Section: A Causal Rule-basementioning

confidence: 99%

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The Relative Accuracy of a Variety of Medical Diagnostic Programs

Todd

Stamper²

1994

Methods Inf Med

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Abstract:Acute abdominal pain is one of the most widely studied applications of computer-aided diagnosis. The usual approach is to apply Bayes’ theorem with the assumption of conditional independence (“independence Bayes”). We compared various approaches to designing diagnostic programs for abdominal pain of suspected gynaecological origin. The methods range from statistical to knowledge-based. All programs were evaluated using a database of 1,270 cases collected retrospectively. Our results suggest that in this application no significant improvement in accuracy can be made by taking interactions into account, either by statistical or by knowledge-based means; independence Bayes is near-optimal. As far as accuracy is concerned, there appears to be little point in pursuing knowledge-based approaches. However, the “nearest neighbours” method using a new metric appears to be at least as accurate as independence Bayes. We argue that the nearest neighbours method is more suitable than independence Bayes for clinical use because of greater accountability.

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“…Hypotension is a cause of facial pallor. (The weight of this rule is different to the zero weight shown in [51)…”

Section: Resultsmentioning

confidence: 88%

Section: A Causal Rule-basementioning

confidence: 99%

The Relative Accuracy of a Variety of Medical Diagnostic Programs

Todd

Stamper²

1994

Methods Inf Med

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show abstract

“…As the authors themselves note, abdominal pain studies now have well over the number of cases needed for maximising diagnosability; how m~y other domains could benefit from similar studies? The simulation model used in the research is described in detail in a more recent publication [5].…”

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confidence: 99%

Knowledge Processing

Jackson-Smale¹

1994

Yearb Med Inform

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A formal model of explanation

Todd

Stamper

1995

Form. Asp. Comput.

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As information systems become more complex, formal methods offer a solution to the increasing problem of ensuring correctness of design and implementation. This paper illustrates the use of mathematical specification to formalise the notion of ‘explanation’. This is relevant to any information system which engages in inference. An information system is regarded as a homogeneous function on states of information. An explanation for a conclusion is regarded as a weakening of the theory relating input to output, such that the same conclusion is reached. As an example, the specification is implemented for a simple statistical classifier to assist medical diagnosis.

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The design and construction of a medical simulation model

Cited by 3 publications

References 29 publications

The Relative Accuracy of a Variety of Medical Diagnostic Programs

The Relative Accuracy of a Variety of Medical Diagnostic Programs

Knowledge Processing

A formal model of explanation

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