The Process of Medical Diagnosis: Routes of Mathematical Investigations

A model of a computer-assisted diagnostic system using fuzzy subsets has been developed. The physician documents symptom—diagnosis presence relationships and symptom—diagnosis conclusiveness relationships by means of labels of the fuzzy subsets never, almost never, very very seldom, very seldom, seldom, more or less seldom, not known, more or less often, often, very often, very very often, almost always, always. Symptoms are regarded as fuzzy subsets of reference sets. The reference set includes all values the symptom may assume. The degree of membership of a value in the fuzzy subset of a symptom is calculated when the patient’s symptom pattern is available. By means of compositions of fuzzy relations, four different diagnostic indications are determined for every diagnosis under consideration: presence indication, conclusiveness indication, non-presence indication and non-symptom presence indication. By performing the diagnostic process, the system provides the physician with proven diagnoses, excluded diagnoses and diagnostic hints, including reasons for the diagnoses displayed. Proposals for further investigations may also be requested.

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A Fuzzy Logical Model of Computer-Assisted Medical Diagnosis

Methods of Information in Medicine ◽

10.1055/s-0038-1635271 ◽

1980 ◽

Vol 19 (03) ◽

pp. 141-148

Author(s):

K.-P. Adlassnig

Keyword(s):

Medical Diagnosis ◽

Diagnostic System ◽

Diagnostic Process ◽

Computer Assisted ◽

Fuzzy Relations ◽

Fuzzy Subset ◽

Symptom Pattern ◽

A Value ◽

Fuzzy Logical Model ◽

Fuzzy Subsets

A model of a computer-assisted diagnostic system using fuzzy subsets has been developed. The physician documents symptom—diagnosis presence relationships and symptom—diagnosis conclusiveness relationships by means of labels of the fuzzy subsets never, almost never, very very seldom, very seldom, seldom, more or less seldom, not known, more or less often, often, very often, very very often, almost always, always. Symptoms are regarded as fuzzy subsets of reference sets. The reference set includes all values the symptom may assume. The degree of membership of a value in the fuzzy subset of a symptom is calculated when the patient’s symptom pattern is available. By means of compositions of fuzzy relations, four different diagnostic indications are determined for every diagnosis under consideration: presence indication, conclusiveness indication, non-presence indication and non-symptom presence indication. By performing the diagnostic process, the system provides the physician with proven diagnoses, excluded diagnoses and diagnostic hints, including reasons for the diagnoses displayed. Proposals for further investigations may also be requested.

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Examples of Structural Pattern Analysis and Medical Image Understanding Application to Medical Diagnosis

Medical Image Understanding Technology - Studies in Fuzziness and Soft Computing ◽

10.1007/978-3-540-40997-7_5 ◽

2004 ◽

pp. 79-134

Author(s):

Ryszard Tadeusiewicz ◽

Marek R. Ogiela

Keyword(s):

Medical Diagnosis ◽

Medical Image ◽

Pattern Analysis ◽

Image Understanding ◽

Structural Pattern ◽

Medical Image Understanding

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A multiperiod hybrid decision support model for medical diagnosis and treatment based on similarities and three‐way decision theory

Expert Systems ◽

10.1111/exsy.12377 ◽

2019 ◽

Vol 36 (3) ◽

pp. e12377 ◽

Cited By ~ 4

Author(s):

Yan Yang ◽

Junhua Hu ◽

Yongmei Liu ◽

Xiaohong Chen

Keyword(s):

Decision Support ◽

Decision Theory ◽

Medical Diagnosis ◽

Diagnosis And Treatment ◽

Decision Support Model ◽

Support Model

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Representation, abstraction, and simple-minded sophisticates

Behavioral and Brain Sciences ◽

10.1017/s0140525x19002942 ◽

2020 ◽

Vol 43 ◽

Author(s):

Peter Dayan

Keyword(s):

Decision Theory ◽

Predictive Coding ◽

Bayesian Decision Theory ◽

Bayesian Decision ◽

Model Based ◽

Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

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Automated Image Analysis Of Nuclear And Cytoplasmic Changes In Exfoliated Cells From Tracheas Treated With Carcinogen

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100078638 ◽

1977 ◽

Vol 35 ◽

pp. 220-221

Author(s):

S.F. Stinson ◽

J.C. Lilga ◽

M.B. Sporn

Keyword(s):

Image Analysis ◽

Pattern Analysis ◽

Relative Proportion ◽

Automated Image Analysis ◽

Image Analyzer ◽

Cross Sectional ◽

Exfoliated Cells ◽

Neoplastic Lesions ◽

Analysis System ◽

Morphologic Criterion

Increased nuclear size, resulting in an increase in the relative proportion of nuclear to cytoplasmic sizes, is an important morphologic criterion for the evaluation of neoplastic and pre-neoplastic cells. This paper describes investigations into the suitability of automated image analysis for quantitating changes in nuclear and cytoplasmic cross-sectional areas in exfoliated cells from tracheas treated with carcinogen.Neoplastic and pre-neoplastic lesions were induced in the tracheas of Syrian hamsters with the carcinogen N-methyl-N-nitrosourea. Cytology samples were collected intra-tracheally with a specially designed catheter (1) and stained by a modified Papanicolaou technique. Three cytology specimens were selected from animals with normal tracheas, 3 from animals with dysplastic changes, and 3 from animals with epidermoid carcinoma. One hundred randomly selected cells on each slide were analyzed with a Bausch and Lomb Pattern Analysis System automated image analyzer.

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An Interactive Minicomputer Program for the Indexing and Simulation of Electron Diffraction Patterns

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100092670 ◽

1976 ◽

Vol 34 ◽

pp. 542-543

Author(s):

R.P. Goehner ◽

W.T. Hatfield ◽

Prakash Rao

Keyword(s):

Electron Diffraction ◽

Real Time ◽

Pattern Analysis ◽

Flow Diagram ◽

Hard Copy ◽

Time Analysis ◽

Real Time Analysis ◽

Transmission Electron ◽

One Step ◽

Diffraction Patterns

Computer programs are now available in various laboratories for the indexing and simulation of transmission electron diffraction patterns. Although these programs address themselves to the solution of various aspects of the indexing and simulation process, the ultimate goal is to perform real time diffraction pattern analysis directly off of the imaging screen of the transmission electron microscope. The program to be described in this paper represents one step prior to real time analysis. It involves the combination of two programs, described in an earlier paper(l), into a single program for use on an interactive basis with a minicomputer. In our case, the minicomputer is an INTERDATA 70 equipped with a Tektronix 4010-1 graphical display terminal and hard copy unit.A simplified flow diagram of the combined program, written in Fortran IV, is shown in Figure 1. It consists of two programs INDEX and TEDP which index and simulate electron diffraction patterns respectively. The user has the option of choosing either the indexing or simulating aspects of the combined program.

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