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Title:HEVRISTIČNO GENERIRANJE MEDICINSKIH SIMULACIJSKIH SCENARIJEV
Authors:ID Križmarić, Miljenko (Author)
ID Kokol, Peter (Mentor) More about this mentor... New window
ID GRMEC, ŠTEFEK (Comentor)
Files:.pdf DR_Krizmaric_Miljenko_2009.pdf (6,10 MB)
MD5: D063988FC1DAD570B90A240E173557FF
PID: 20.500.12556/dkum/85bc8341-0481-4ca3-821d-7d767e666813
 
Language:Slovenian
Work type:Dissertation
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Sodobno izobraževanje zdravstvenih delavcev zahteva uporabo medicinskih simulatorjev, saj medicina postaja zahtevna in kompleksna. Simulatorji potrebujejo scenarije, ki jih izdelujejo zdravniki, vendar je tak pristop subjektiven, ker ima vsak zdravnik svoje lastne izkušnje. Scenarije lahko izboljšamo z uporabo metod strojnega učenja. V doktorskem delu raziščemo in uporabimo Bayesove verjetnostne mreže kot računalniško podprto orodje za izdelavo scenarijev. Predstavimo metodologijo CasGEN, ki jo uporabimo na realnem primeru večje podatkovne zbirke s področja urgentne medicine - 737 primerov predbolnišničnega oživljanja. Rezultate validiramo s pomočjo trifaznega modela zastoja srca pri prekatni fibrilaciji. V doktorskem delu potrdimo ustreznost metodologije CASGen na podlagi študije, ki so jo pozitivno ocenili eksperti s področja medicine. Na tak način potrdimo hipotezo raziskovalnega dela, da je predstavljena metodologija CASGen uporabno orodje za generiranje medicinskih scenarijev.
Keywords:medicinske simulacije, simulatorji, scenariji, strojno učenje, Bayesove verjetnostne mreže, urgentna medicina
Place of publishing:Maribor
Publisher:[M. Križmarić]
Year of publishing:2009
PID:20.500.12556/DKUM-10010 New window
UDC:[004.94:61]:004.8(043.3)
COBISS.SI-ID:244925440 New window
NUK URN:URN:SI:UM:DK:KL04YYCE
Publication date in DKUM:26.03.2009
Views:4302
Downloads:635
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Secondary language

Language:English
Title:Heuristic generation of medical simulation scenarios
Abstract:Modern education in health care today can not exist without the use medical simulators, which are state of the art for efficient learning. Simulators use scenarios, which are designed by physician’s own experiences and presents a subjective approach by scenario design. In doctoral thesis machine learning methods for building scenarios from real data are presented. In particularly we introduce Bayesian probability networks as the primary tool for scenario design. We present a methodology CASGen (Computer Aided Scenario Generation) which uses Bayesian network (BN) and apply it of real study of emergency medicine, where 737 cases in prehospital resuscitation were collected. The results of CASGen are validated with the three phase model for ventricular fibrillation. We confirm the hypothesis of the research that BN are appropriate method for supporting scenario design. The application and use of CASGen was independently confirmed by experts from medical domain.
Keywords:medical simulations, simulators, scenarios, machine learning, bayesian probability networks, emergency medicine


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