Symptom diagnosis: Optimal subsets for upper abdominal pain

Scheinok, Perry A.; Rinaldo, Joseph A.

doi:10.1016/s0010-4809(67)80010-7

Cited by 31 publications

(4 citation statements)

References 7 publications

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“…These data were collected by Rinaldo, Scheinok, and Rupe(83) in 1963; each patient was labeled unequivocally using conventional medical diagnosis, so the data affords a natural basis for k -NN designs. A subsequent study of classifier design using these data was done by Scheinok and Rinaldo, (94) who gave empirical error rates based on various subsets of selected features. Later, Toussaint and Sharpe(107) studied different methods of estimation of empirical error rates using k-NN designs as the classifier basis.…”

Section: S27 Nearest Prototype Classifiers: Diagnosis Of Stomach Dismentioning

confidence: 99%

Pattern Recognition with Fuzzy Objective Function Algorithms

Bezdek¹

1981

12,648

6,640

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holds a B.S. in Civil Engineering from the University of Nevada (Reno) and a Ph.D. in Applied Mathematics from Cornell University. His professional interests currently include mathematical theories underlying problems in cluster analysis, cluster validity, feature selection, and classifier design; applications in medical diagnosis, geologic shape analysis, and sprinkler networks; and the numerical analysis of low-velocity hydraulic systems. Professor Bezdek has held research grants from NSF and ONR and has been a pattern recognition consultant for several industrial firms. He is an active member of many professional groups, including the IEEE Computer Society.A Continuation Order Plan is available for this series. A continuation order will bring delivery of each new volume immediately upon publication. Volumes are billed only upon actual shipment. For further information please contact the publisher.

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Section: S27 Nearest Prototype Classifiers: Diagnosis Of Stomach Dismentioning

confidence: 99%

Pattern Recognition with Fuzzy Objective Function Algorithms

Bezdek¹

1981

12,648

6,640

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“…Nearly 30 years later, it remains uncomfortably true that, in recent years, the symptomatology of upper gastrointestinal disease has been to a large extent ignored. Moreover (with the exception of a small number of outstanding studies-for example, Edwards and Coghill, 1968;Scheinok and Rinaldo, 1967;Bouchier et al, 1968;Myren and Serck-Hanssen 1974;Cleator etal., 1973;Earlam, 1976)whatliteraturehas appeared has consisted of clinical impression oraphorismrather than observed findings in a large series of patients.…”

Section: Discussionmentioning

confidence: 99%

“…Less than half of the patients with either duodenal or gastric ulcer complained of pain which was related to meals (in the sense that eating aggravated the pain), while 53 % of the patients we studied claimed that eating did not affect their pain. Moreover, in Scheinok and Rinaldo's (1967) series less than 33 % of the gastric ulcer patients, and under 5 % of the duodenal ulcer patients, claimed that eating made their symptoms worse. Earlam (1976) studying solely duodenal ulcer patients and controls showed similar trends.…”

Section: Discussionmentioning

confidence: 99%

Clinical presentation of patients with "dyspepsia". Detailed symptomatic study of 360 patients.

Horrocks¹,

Dombal²

1978

Gut

121

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“…The second type of application involves the situation where we are willing to assume a one-to-one correspondence between the K" empirical disease categories and the K underlying diseases. Such a linkage might be established by using an initial cluster analysis to determine empirically the K':• disease categories [12J or on the basis of clinical theory and practice [10]. Knowledge of the ultimate diagnostic categories allows the assignment of each of the N patients to one of the K" groups.…”

Section: Key-wordsmentioning

confidence: 99%

A New Procedure for Analysis of Medical Classification

Woodbury¹,

Mantón²

1982

Methods Inf Med

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A number of classification techniques have been applied to the analysis of medical diagnostic systems and decision making. Commonly used approaches such as cluster analysis, linear discriminant analysis and Bayesian classification are subject to logical and statistical limitations. In this paper we present a methodology, called »grade of membership« analysis, which resolves many of those limitations. This methodology deals simultaneously with the dual problems of case clustering and estimation of discriminant coefficients. The methodology also permits the assessment of the reliability of externally defined medical diagnoses, multiple diagnoses for individuals, disease progression and severity, and permits the representation of patient heterogeneity within diagnostic category. Maximum likelihood principles are invoked both to obtain parameter estimates and as a basis for likelihood ratio testing of complex hypotheses about the model structure. The model is illustrated by an analysis of data on abdominal symptoms and disease.

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Symptom diagnosis: Optimal subsets for upper abdominal pain

Cited by 31 publications

References 7 publications

Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms

Clinical presentation of patients with "dyspepsia". Detailed symptomatic study of 360 patients.

A New Procedure for Analysis of Medical Classification

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