Multicriteria similarity models for medical diagnostic support algorithms

• Autorzy: Ameljańczyk A.
• Języki publikacji: EN

Abstrakty

EN

The paper presents a general procedure model for the identification of diagnostic medical patterns based on multicriteria assessment of similarity. A general similarity detection area was defined, in which a pattern recognition optimization problem was formulated. An exemplary algorithm supporting the process of determining the initial medical diagnosis based on the identified disease symptoms and risk factors is presented. The presented algorithm allows for determining a set of diseases from which there is none more probable, and their ranking.

Słowa kluczowe

EN

disease pattern; indicators and relations of similarity; pattern recognition; similarity; Tversky similarity model medical diagnosis

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2013
• Tom: Vol. 9, no. 1
• Strony: 1--7
• Opis fizyczny: Bibliogr. 20 poz.

Twórcy

autor

Ameljańczyk A.

• Faculty of Cybernetics, Military University of Technology, 2 Kaliskiego Street, 00-908 Warsaw, Poland

Bibliografia

• 1. Ameljańczyk A. Multicriteria optimization in control and management problems. Wrocław: Ossolineum, 1984.
• 2. Ameljańczyk A. Mathematical model medical diagnosis support of based on disease symptoms and risk factors. VII-th Modeling Biological Cybernetic Systems Science Conference, MCSB2010, Kraków 2010.
• 3. Ameljańczyk A. Mathematical model of space of life in medical decision support systems. 1-st National Conference “ Computer Systems and Information and Communications Technology in Healthcare”, Warsaw 2009.
• 4. Błaszczykowski J, Krawiec K, Słowiński R, Stefanowski J, Wilk Sz. Supporting decisions and communication in telemedical systems. Poznań : Wydawnictwo Polit. Pozn., 2006.
• 5. Oniśko A and others. HEPAR I HEPAR II – Computer systems supporting the diagnosis of liver disease. XII-th Biocybernetics and Biomedical Engineering Conference, Warsaw, 2001.
• 6. Albin M. Fuzzy sets and their applications to medical diagnosis. Ph.D. Thesis, University of Carolina, Berkeley, 1975.
• 7. Smets P. Medical diagnosis fuzzy sets and degrees of belief. Fuzzy set Syst 1981;5.
• 8. Allan M. Crash course – interview and physical examination Wrocław: Elsevier Urban & Partner, 2005.
• 9. Kokot F. Differential diagnosis of disease symptoms. Warsaw: WL PZWL, 2007.
• 10. Siegenthaler W. Differential diagnosis in internal medicine. Warsaw: Medipage, 2009;1– 2.
• 11. Collins R. Douglas. Algorithms and interpreting clinical symptoms. Warsaw: Medipage, 2010.
• 12. Kokot F, Kokot S. Laboratory tests – the scope of standards and interpretation. Warsaw: WL PZWL, 2002.
• 13. Beynon HLC, others. Interpreting clinical data in case queries and descriptions. Wrocław: Elsevier Urban & Partner, 2007.
• 14. The Merck Manual. Clinical symptoms. Wrocław: Elsevier Urban & Partner, 2010.
• 15. Ameljańczyk A, Długosz P, Strawa M. Computer implementation of the algorithm of determining the initial medical diagnosis. VII-th Modeling Biological Cybernetic Systems Science Conference, MCSB2010, Kraków 2010.
• 16. Yu PL, Leitmann G. Compromise solutions, domination structures and Salukwadze ’ s solution. JOTA 1974;13.
• 17. Pawlak Z. Rough sets. Int J Comput Inf Sci 1965;11:341 – 56.
• 18. Makal J. Expert system for supporting diagnosis of benign prostatic hyperplasia. Automation and Robotics Measurement 2004;7 – 8.
• 19. Sanchez E. Medical diagnosis and composite fuzzy relations. In: Gupta MM, Ragade RK, editors. Advances in fuzzy sets theory and applications. North – Holland, Amsterdam, 1979.
• 20. Wechsler H. Applications of fuzzy logic to medical diagnosis. Proc. Symp. on Multiple – Valned Logic Logan, 1975.