Review of Pattern Recognition in Medical Diagnosis and Consulting Relative to a New System Model

Patrick, Edward A.; Stelmack, Frank P.; Shen, Leon Y. L.

doi:10.1109/tsmc.1974.5408515

Cited by 64 publications

(8 citation statements)

References 105 publications

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“…A wide range of medical problems have been attacked by "computer-aided diagnosis", as it is usually known. Wagner et al (1978) provide a bibliography of over 800 references up to 1977, although since then applications have been decreasing steadily: Index Medieus cited 30 papers under "Diagnosis: Computer-assisted" in 1982Computer-assisted" in compared with 83 in 1977 In this section we have only space to give some examples of the type of statistical models that have been found useful; for further reviews see Patrick et al (1974), Wardle and Wardle (1978), Rogers et al (1979), Shortliffe et al (1979) and Titterington et al (1981).…”

Section: Statistical Systemsmentioning

confidence: 99%

“…On a technical note, means of incorporating interactions into the independence model have been discussed by Titterington et al (1981), and Pantin and Merrett (1982) provide a recent example using clusters of dependent symptoms. More complex techniques include the pattern recognition approach of Patrick et al (1974), nearest-neighbour models in Coomans et al (1983) and kernel density estimation in Habbema et al (1978). In contrast to the complexity of these models, Goldman et al (1982) recently used recursive partitioning to produce a simple branching tree structure for decision-making.…”

Section: Statistical Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Statistical and Knowledge-Based Approaches to Clinical Decision-Support Systems, with an Application in Gastroenterology

Spiegelhalter¹,

Knill‐Jones²

1984

Journal of the Royal Statistical Society. Series A (General)

288

141

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Many attempts have been made to support clinical decisions by formal statistical reasoning, but the practical impact of these efforts has been limited. Developers of "expert systems", who use the techniques of artificial intelligence to represent clinicians' personal "knowledge", have suggested one reason for this lack of success may be that the probabilistic methodology itself is often inappropriate to the clinical problems or opaque to the user. We contrast the statistical and "knowledge-based" paradigms, with an emphasis on their different approaches to the manipulation and explanation of uncertainty. A statistical application to the diagnosis of "dyspepsia" is described, in which data are obtained by computer interview of the patient, and both diseases and symptoms form a hierarchy. We argue that the flexible use of "weights of evidence" overcomes many of the previous criticisms of statistical systems while retaining a valid probabilistic output. We conclude by discussing the complementary roles of deductive and probabilistic reasoning.

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Section: Statistical Systemsmentioning

confidence: 99%

Section: Statistical Systemsmentioning

confidence: 99%

Statistical and Knowledge-Based Approaches to Clinical Decision-Support Systems, with an Application in Gastroenterology

Spiegelhalter¹,

Knill‐Jones²

1984

Journal of the Royal Statistical Society. Series A (General)

288

141

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“…The physicians were expert in the management of SLE. A third source of predictors was the ARAMIS »Autoset" technique, which locates the 40 most nearly similar patients in the data bank, using a »multi-dimensional nearest neighbor" statistic [20]. This technique determines the frequency of the outcome in that group, and uses this frequency as a prediction of the probability of the outcome in a new patient not in the training group.…”

Section: Clinical Accuracymentioning

confidence: 99%

Determining the Most Valuable Clinical Variables : a Stepwise Multiple Logistic Regression Program

Coles¹,

Brown²,

Engelhard³

et al. 1980

Methods Inf Med

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In solving a clinical problem of diagnosis, prognosis, or treatment choice, a physician must select from among a large group of possible tests. In general, an ordering exists specifying which tests are most valuable in providing relevant information concerning the problem on hand. The computer program package to be described (MW) extracts appropriate data from the ARAMIS data banks and then analyzes the data by stepwise logistic regression. A binary outcome (diagnosis, prognostic event, or treatment response) is sequentially associated with possible tests, and the most powerful combination of tests is identified. For example, the most valuable predictor variable of early mortality in SLE is proteinuria, followed sequentially by anemia and absence of arthritis. Experience with these techniques suggests : 1. optimal certainty is usually reached after only three or four tests; 2. several different test sequences may lead to the same level of certainty; 3. diagnosis may usually be ascertained with greater certainty than prognosis; 4. many medical problems contain considerable non-reducible uncertainty; 5. a relatively small group of tests are typically found among the most powerful; 6. results are consistent across several patient populations; 7. results are largely independent of the particular statistic employed. These observations suggest strategies for maximizing information while minimizing risk and expense.

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“…[41] wrote in 1970 that »the very notion of a disease is so nebulous, however, that to deal with it in terms of recognition algorithms may just give the discussion a superficial and spurious exactness«, many papers on pattern recognition in medical diagnosis have already been written. A review on this topic is to be found in [65].…”

Section: The Essential Elements Of ~Iedical Diagnosismentioning

confidence: 99%

The Process of Medical Diagnosis: Routes of Mathematical Investigations

Tautu¹,

Wagner²

1978

Methods Inf Med

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Review of Pattern Recognition in Medical Diagnosis and Consulting Relative to a New System Model

Cited by 64 publications

References 105 publications

Statistical and Knowledge-Based Approaches to Clinical Decision-Support Systems, with an Application in Gastroenterology

Statistical and Knowledge-Based Approaches to Clinical Decision-Support Systems, with an Application in Gastroenterology

Determining the Most Valuable Clinical Variables : a Stepwise Multiple Logistic Regression Program

The Process of Medical Diagnosis: Routes of Mathematical Investigations

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