Literature DB >> 16956755

From vagueness in medical thought to the foundations of fuzzy reasoning in medical diagnosis.

Rudolf Seising1.   

Abstract

OBJECTIVE: This article delineates a relatively unknown path in the history of medical philosophy and medical diagnosis. It is concerned with the phenomenon of vagueness in the physician's "style of thinking" and with the use of fuzzy sets, systems, and relations with a view to create a model of such reasoning when physicians make a diagnosis. It represents specific features of medical ways of thinking that were mentioned by the Polish physician and philosopher Ludwik Fleck in 1926. The paper links Lotfi Zadeh's work on system theory before the age of fuzzy sets with system-theory concepts in medical philosophy that were introduced by the philosopher Mario Bunge, and with the fuzzy-theoretical analysis of the notions of health, illness, and disease by the Iranian-German physician and philosopher Kazem Sadegh-Zadeh. MATERIAL: Some proposals to apply fuzzy sets in medicine were based on a suggestion made by Zadeh: symptoms and diseases are fuzzy in nature and fuzzy sets are feasible to represent these entity classes of medical knowledge. Yet other attempts to use fuzzy sets in medicine were self-contained. The use of this approach contributed to medical decision-making and the development of computer-assisted diagnosis in medicine.
CONCLUSION: With regard to medical philosophy, decision-making, and diagnosis; the framework of fuzzy sets, systems, and relations is very useful to deal with the absence of sharp boundaries of the sets of symptoms, diagnoses, and phenomena of diseases. The foundations of reasoning and computer assistance in medicine were the result of a rapid accumulation of data from medical research. This explosion of knowledge in medicine gave rise to the speculation that computers could be used for the medical diagnosis. Medicine became, to a certain extent, a quantitative science. In the second half of the 20th century medical knowledge started to be stored in computer systems. To assist physicians in medical decision-making and patient care, medical expert systems using the theory of fuzzy sets and relations (such as the Viennese "fuzzy version" of the Computer-Assisted Diagnostic System, CADIAG, which was developed at the end of the 1970s) were constructed. The development of fuzzy relations in medicine and their application in computer-assisted diagnosis show that this fuzzy approach is a framework to deal with the "fuzzy mode of thinking" in medicine.

Entities:  

Mesh:

Year:  2006        PMID: 16956755     DOI: 10.1016/j.artmed.2006.06.004

Source DB:  PubMed          Journal:  Artif Intell Med        ISSN: 0933-3657            Impact factor:   5.326


  13 in total

1.  A fuzzy probabilistic method for medical diagnosis.

Authors:  D K Mak
Journal:  J Med Syst       Date:  2015-02-10       Impact factor: 4.460

2.  The fuzzy brain. Vagueness and mapping connectivity of the human cerebral cortex.

Authors:  Philipp Haueis
Journal:  Front Neuroanat       Date:  2012-09-05       Impact factor: 3.856

3.  Fuzzy obesity index (MAFOI) for obesity evaluation and bariatric surgery indication.

Authors:  Susana Abe Miyahira; João Luiz Moreira Coutinho de Azevedo; Ernesto Araújo
Journal:  J Transl Med       Date:  2011-08-14       Impact factor: 5.531

4.  Multivariate modeling to identify patterns in clinical data: the example of chest pain.

Authors:  Oliver Hirsch; Stefan Bösner; Eyke Hüllermeier; Robin Senge; Krzysztof Dembczynski; Norbert Donner-Banzhoff
Journal:  BMC Med Res Methodol       Date:  2011-11-22       Impact factor: 4.615

5.  A noninvasive method for coronary artery diseases diagnosis using a clinically-interpretable fuzzy rule-based system.

Authors:  Hamid Reza Marateb; Sobhan Goudarzi
Journal:  J Res Med Sci       Date:  2015-03       Impact factor: 1.852

6.  Assessing experience in the deliberate practice of running using a fuzzy decision-support system.

Authors:  Maria Isabel Roveri; Edison de Jesus Manoel; Andrea Naomi Onodera; Neli R S Ortega; Vitor Daniel Tessutti; Emerson Vilela; Nelson Evêncio; Isabel C N Sacco
Journal:  PLoS One       Date:  2017-08-17       Impact factor: 3.240

7.  Predicting the Risk of Mortality in Children using a Fuzzy-Probabilistic Hybrid Model.

Authors:  Corsino Rey; Juan Mayordomo-Colunga; Roberts Gobergs; Reinis Balmaks; Ana Vivanco-Allende; Andrés Concha; Alberto Medina; Ana Colubi; Gil González-Rodríguez
Journal:  Biomed Res Int       Date:  2022-03-03       Impact factor: 3.411

8.  An extensible six-step methodology to automatically generate fuzzy DSSs for diagnostic applications.

Authors:  Antonio d'Acierno; Massimo Esposito; Giuseppe De Pietro
Journal:  BMC Bioinformatics       Date:  2013-01-14       Impact factor: 3.169

9.  Towards a unified theory of health-disease: II. Holopathogenesis.

Authors:  Naomar Almeida Filho
Journal:  Rev Saude Publica       Date:  2014-04       Impact factor: 2.106

10.  A Boolean Consistent Fuzzy Inference System for Diagnosing Diseases and Its Application for Determining Peritonitis Likelihood.

Authors:  Ivana Dragović; Nina Turajlić; Dejan Pilčević; Bratislav Petrović; Dragan Radojević
Journal:  Comput Math Methods Med       Date:  2015-11-17       Impact factor: 2.238

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.