Statistical evolutionary optimisation of pharmacological therapies

• Autorzy: Ossowski A.
• Języki publikacji: EN

Abstrakty

EN

Optimisation of pharmacological therapies is an important problem of modern medicine. A lot of data concerning large groups of patients has to be analysed within a few months or years in order to find (sub)optimal therapeutic standards. Since new effective drugs become available each year, natural “brain storm” iterations in the world community of clinicians for new trials generation and co-ordination may be insufficient to perform the optimisation in real time. Therefore computer supervision of such a world-wide process is necessary. Basic problems concerning complex medical therapies and their optimisation have been described in this work. A formal description of the optimisation problem has been provided. Qualitative and quantitative genetic operations (mutation, crossover) have been distinguished. The idea of statistical fitnes functions has been introduced. Statistical genetic algorithms as appropriate optimisation procedures has been proposed. The usefulness of application of the described algorithms in medicine has been pointed out. The block structure of a computer system for trials generation and coordination has been proposed.

Słowa kluczowe

EN

genetic algorithm; optimisation; mutation; crossover; selection; medical therapy

PL

algorytm genetyczny; optymalizacja; mutacja; krzyżowanie; wybór; terapia medyczna

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2001
• Tom: Vol. 2, Part. 1
• Strony: MI67--MI73
• Opis fizyczny: Bibliogr. 5 poz., rys.

Twórcy

autor

Ossowski A.

• Institute of Fundamental Technological Research PAS, 00-049 Warszawa, ul. Świętokrzyska 21

Bibliografia

• [1] BUJKO K., NOWACKI P.M., MICHALSKI W., Generalizability of results of clinical trials, Nowotwory, tom 50, Zeszyt 3 / pp. 255-258, 2000.
• [2] MADEJ G., Chemioterapia onkologiczna dorosłych i dzieci, PZWL, Warszawa 1994.
• [3] OSSOWSKI A., A formal approach to rules interpretation working in medical databases, Proceeding of the international conference TIM2001, Ustroń-Poland.
• [4] OSSOWSKI A., Statistical genetic algorithms. pp. 143-154, Advances in Soft Computing, Intelligent Information Systems, Physica-Verlag 2001.
• [5] MICHALSKI R.S., CERVONE G. and KAUFMAN K., Speeding up evolution through learning: LEM, pp. 243-256, Advances in Soft Computing, Intelligent Information Systems, Physica-Verlag 2000.