A probabilistic rule-based expert system

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

doi:10.1016/0020-7101(93)90030-a

Cited by 11 publications

(5 citation statements)

References 29 publications

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“…A previous urinary infection makes a subsequent one slightly more likely, and urinary infections are commoner in pregnancy owing to urinary stasis. (The weight of this rule is higher than shown in [49) This identifies two specific symptoms of a urinary tract infection: urinary frequency and pain on passing urine ("dysuria"). Since pregnancy is also a cause of frequency, the latter is relevant only if the patient is not pregnant.…”

Section: Resultsmentioning

confidence: 98%

“…Thus if V has h possible (i. e., legal) values, then: has often been adopted for expert systems (e. g., [24,[43][44][45]), although it is used less often now because of criticism of the way uncertainty is handled (e. g., [37,[46][47][48]), Bayesian networks generally being preferred. However, we have recently shown how a collection of weighted inference rules can be given a sound probabilistic interpretation in terms of a Bayesian network [49]. The nodes of the Bayesian network are atomic propositions: each is an assertion that the value of a variable lies in a specified set of possible values.…”

Section: Contruction Of the Networkmentioning

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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Section: Resultsmentioning

confidence: 98%

Section: Contruction Of the Networkmentioning

confidence: 99%

The Relative Accuracy of a Variety of Medical Diagnostic Programs

Todd

Stamper²

1994

Methods Inf Med

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“…One of the reasons for this failure can be understood in relation to the lack of models that capture the depth and complexity of expert medical diagnostic reasoning. Models previously proposed for medical diagnostic reasoning include: scheme-inductive reasoning [2]; hypothetico-deductive reasoning [3]; backward and forward reasoning [4]; pattern recognition [5]; Parsimonious Covering Theory [6]; Information Processing Approach [7]; Process Model for diagnostic reasoning [8]; Certainty Factor model [9]; models based on Bayes Theorem [10][11][12]; and models based on Fuzzy logic [13][14][15]. The authors have previously described the limitations of some of these approaches, and proposed an approximate reasoning model for medical diagnostic reasoning [16].…”

Section: Introductionmentioning

confidence: 99%

ST Algorithm for Medical Diagnostic Reasoning

Fernando¹,

Henskens²

2013

Polibits

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The authors have previously described an approach for medical diagnostic reasoning based on the ST (Select and Test) model introduced by Ramoni and Stefanelli et al. This paper extends the previous approach by introducing the required algorithm for medical expert system development. The algorithm involves a bottom-up and recursive process using logical inferences, abduction, deduction, and induction. Pseudocode for the algorithm, and the data structures involved, are described, and the algorithm's implementation using a small sample knowledgebase and programmed in Java is included in appendixes. Implementation of a successful expert system is a challenging process; development of the necessary algorithm for its inference engine, and definition of a knowledgebase structure that models expert diagnostic reasoning and knowledge, only fulfils the initial step. Challenges associated with the remaining steps of the development process can be identified and dealt with using the CLAP software process model.

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“…In one study, clinicians using an expert system (compared with conventional practice) ordered fewer laboratory tests during the diagnostic process, completed the diagnostic workup with fewer sample collections, generated lower laboratory costs, shortened the time required to reach a diagnosis, showed closer adherence to established clinical practice guidelines, and exhibited a more uniform and diagnostically successful investigation (231). Expert systems have been used to diagnose a variety of clinical conditions, including community-acquired pneumonias (9,279), septicemia (203), female genital disease and abdominal pain (263), urinary tract infection (51,281), viral (121) and infantile (90) meningitis, febrile tropical diseases (27), chronic prostatitis (31), infective endocarditis (76), and infectious diseases (266). Some systems have used fuzzy-logic methods (14); others have used the World Wide Web (76).…”

Section: Introductionmentioning

confidence: 99%

Expert Systems in Clinical Microbiology

Winstanley

Courvalin

2011

Clin Microbiol Rev

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SUMMARY This review aims to discuss expert systems in general and how they may be used in medicine as a whole and clinical microbiology in particular (with the aid of interpretive reading). It considers rule-based systems, pattern-based systems, and data mining and introduces neural nets. A variety of noncommercial systems is described, and the central role played by the EUCAST is stressed. The need for expert rules in the environment of reset EUCAST breakpoints is also questioned. Commercial automated systems with on-board expert systems are considered, with emphasis being placed on the “big three”: Vitek 2, BD Phoenix, and MicroScan. By necessity and in places, the review becomes a general review of automated system performances for the detection of specific resistance mechanisms rather than focusing solely on expert systems. Published performance evaluations of each system are drawn together and commented on critically.

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A probabilistic rule-based expert system

Cited by 11 publications

References 29 publications

The Relative Accuracy of a Variety of Medical Diagnostic Programs

The Relative Accuracy of a Variety of Medical Diagnostic Programs

ST Algorithm for Medical Diagnostic Reasoning

Expert Systems in Clinical Microbiology

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