B.A. Kobrinskiy

Participation of the physician at decision support system by intellectual system is positive aspect. It can be management of diagnostics modes, revaluation of coefficient for signs at hypothesis construction, transformation of a diagnostic series by expert system. Knowledge extraction on Data Mining technology should be accompanied by the analysis received given (templates, etc.) with attraction of experts. Construction of intellectual system for hundreds and thousand illnesses assumes a group way for knowledge extraction. In this case it is necessary to use a principle supplementary the knowledge received from separate experts and from other sources. For this purpose various mechanisms can be used. The question of comparison of opinions of various research schools of thought as a part of expert systems demands the decision. The expediency of transition to hybrid architecture with inclusion of precedents is caused by increase in diseases with an atypical clinical picture. Separate aspects for efficiency increase can be found in earlier created expert systems. At the same time, till now, with rare exception, there are no intellectual dynamic medical systems. Systems of new generation should ensure functioning in real time that is necessary at urgent conditions. Prospects of development of medical intellectual systems should include joint diagnostics of the core and accompanying diseases, construction of open knowledge bases, to provide transition to lingvo-images knowledge bases in which images should supplement verbal characteristics at formation of intermediate and definitive hypotheses.

 

1. JAspers K. Obshhaja psikhopatologija. / Per. s nem. L. O. Akopjana. M.: Praktika. 1997. S. 266 -268.
2. SHapiro D. I. Prinjatie reshenijj v sistemakh organizacionnogo upravlenija: ispolzovanie rasplyvchatykh kategorijj. M.: EHnergoatomizdat. 1983. 415 s.
3. Narinjani A. S. Ne-faktory: kratkoe vvedenie // Novosti iskusstvennogo intellekta. 2004. № 2. S. 52 - 63.
4. Fayyad U. M., Irani K. B.Multi-interval discretization of continuous-valued attributes for classification learning // Proc. Thirteenth International Joint Conference on Artificial Intelligence. SanFrancisco: MorganKaufmannPubl. Inc. 1993. P. 1022 - 1027.
5. Kobrinskijj B. A. Izvlechenie ehkspertnykh znanijj: gruppovojj variant // Novosti iskusstvennogo intellekta. 2004. № 3. S. 58 - 66.
6. Mill Dzh. St. Sistema logiki sillogisticheskojj i induktivnojj: Izlozhenie principov dokazatelstva v svjazi s metodami nauchnogo issledovanija. M.: Lenand. 2011. 832 s.
7. Kobrinskijj B. A. Logika argumentacii v prinjatii reshenijj v medicine // NTI. Ser. 2: Informacionnye processy i sistemy. 2001. № 9. S. 1 - 8.
8. Esenin-Volpin A. S. Ob antitradicionnojj (ultraintuicionistskojj) programme osnovanijj matematiki i estestvennonauchnom myshlenii. // Semiotika i informatika. 1993. Vyp. 33. S. 13 - 67.
9. Marjanchik B. V. O primenenii virtualnykh setejj i mjagkikh vychislenijj v medicinskojj diagnostike. // VI Nac. konf. po iskusstvennomu intellektu s mezhdunar. uchastiem KII-98: Trudy konferencii. Pushhino: 1998. T. 1. S. 319 - 325.
10. Adlassnig K. P., Scheithauer W., Grabner G. CADIAG-2/PANCREAS: an artificial intelligence system based on fuzzy set theory to diagnose pancreatic diseases // 3^rdInternational conference system science in health care. Munich, July, 1984. Berlin e.a. 1984. P.396-399.
11. Chandrasekaran B., Mittal S., Smith J. W. MDX and related medical decision-making systems // Proc. International joint conference on artificial intelligence. V. 2. San Francisco: Morgan Kaufmann Publ. Inc. 1981. P.1055-1055.
12. Citro G., Banks G., Cooper G. INKBLOT: a neurological diagnostic decision support system integrating causal and anatomical knowledge // Artificial intelligence medicine. 1997. V. 10. № 3. P. 257 - 267.
13. Meshalkin L. D., Goldberg S. I. Novyjj klass sistem iskusstvennogo intellekta (DrWt-sistemy) // Izvestija RAN. Ser. «Tekhnicheskaja kibernetika». 1992. № 5. S. 217 - 223.
14. Bokerija L. A., Lishhuk V. A., Gazizova D. SH. i dr. Matematicheskie modeli serdca, krovoobrashhenija i dykhanija v ehksperimentalnykh i klinicheskikh issledovanijakh: obobshhenie tridcatiletnego opyta // Bjulleten NC SSKH im. A. N. Bakuleva RAMN. 2003. T. 4. № 2. S. 28 - 33.
15. Kobrinskijj B. A., Taperova L. N. Proekt medicinskojj intellektualnojj sistemy realnogo vremeni dlja reanimacii // Nauch. sessija MIFI-2007: Sb. nauch. tr. M.: 2007. T. 3. S. 32 - 34.
16. Rybina G. V., Parondzhanov S. S. Tekhnologija postroenija dinamicheskikh intellektualnykh sistem. M.: NIJAU MIFI. 2011. 240 s.
17. Finn V. K. Pravdopodobnye rassuzhdenija v intellektualnykh sistemakh tipa DSM. DSM-metod avtomaticheskogo porozhdenija gipotez: Logicheskie i ehpistemiologicheskie osnovanija / Sost. O. M. Anshakov, E. F. Fabrikantova // pod red. O. M. Anshakova. M.: LIBROKOM. 2009. S. 10 - 50.
18. Kobrinskijj B. A. Znachenie vizualnykh obraznykh predstavlenijj dlja medicinskikh intellektualnykh sistem // Iskusstvennyjj intellekt i prinjatie reshenijj. 2012. № 3. S. 3 - 8.