Inteligentno sučelje za unos medicinskih podataka

Šimunić, Krešimir

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scientific master's theses

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Intelligent Interface for Medical Data Entry

Krešimir Šimunić (2000)

University of Zagreb
Faculty of Electrical Engineering and Computing
Department of Applied Mathematics

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APA 6th Edition

Šimunić, K. (2000). Inteligentno sučelje za unos medicinskih podataka (Scientific master's theses). Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:322521

MLA 8th Edition

Šimunić, Krešimir. "Inteligentno sučelje za unos medicinskih podataka." Scientific master's theses, University of Zagreb, Faculty of Electrical Engineering and Computing, 2000. https://urn.nsk.hr/urn:nbn:hr:168:322521

Chicago 17th Edition

Harvard

Šimunić, K. (2000). 'Inteligentno sučelje za unos medicinskih podataka', Scientific master's theses, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 16 June 2019, https://urn.nsk.hr/urn:nbn:hr:168:322521

Vancouver

Šimunić K. Inteligentno sučelje za unos medicinskih podataka [Scientific master's theses]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2000 [cited 2019 June 16] Available at: https://urn.nsk.hr/urn:nbn:hr:168:322521

IEEE

K. Šimunić, "Inteligentno sučelje za unos medicinskih podataka", Scientific master's theses, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2000. Available at: https://urn.nsk.hr/urn:nbn:hr:168:322521

Cite this item: https://urn.nsk.hr/urn:nbn:hr:168:322521

Metadata

Title	Inteligentno sučelje za unos medicinskih podataka
Title (english)	Intelligent Interface for Medical Data Entry
Author	Krešimir Šimunić
Mentor	Damir Kalpić (mentor)
Committee member	Vedran Mornar (član povjerenstva)
Committee member	Damir Kalpić (član povjerenstva)
Committee member	Vlatko Čerić (član povjerenstva)
Granter	University of Zagreb Faculty of Electrical Engineering and Computing (Department of Applied Mathematics) Zagreb
Defense date and country	2000-06-07, Croatia
Scientific / art field, discipline and subdiscipline	TECHNICAL SCIENCES Computing
Universal decimal classification (UDC)	004 GENERALLY Computer science and technology. Computing. Data processing
Abstract	Uobičajeni unos podataka temeljem izbora iz zatvorenog skupa, a uz uporabu standardnih izbornika, u medicini je neprikladan zbog velike kardinalnosti takvih skupova. Međutim, ako bi se povećala učinkovitost izbornika, njihova uporaba imala bi dodatnih prednosti u odnosu na unos slobodnog teksta. Apriorna vjerojatnost da će se pacijentu propisati neki od mnoštva lijekova nije velika, ali ako se zna od čega pacijent boluje, aposteriorna se vjerojatnost za određene lijekove znatno povećava. Podijeljeni izbornik je sučelje koje pokazuje nekoliko najvjerojatnijih stavki na vrhu liste (inteligentna lista), a potpuni skup stavki u preostalom dijelu liste. Iz literature znamo da su razvijena i analizirana 4 modela strojnog učenja (frekvencijski model, Bayesov model, model linearne regresije, model diskriminantne analize) za oblikovanje inteligentne liste. U radu je obrađena teoretska pozadina modela i analizirana karakteristika tih izbornika. Kao potencijalno bolji model, razvijen je model logističke regresije te inteligentni izbornik baziran na modelu logističke regresije i Bayesovom modelu. Uspoređuju se karakteristike tih modela. Dane su smjernice daljnjih istraživanja.
Abstract (english)	Due to the vast size of medical concepts, their entry from conventional menus is obviously problematic. If possible, however, menu-based entry has advantages over free-text processing. In the medical domain, there is a possibility to use conditional probability. A priori probability for prescription of a certain drug from the set of drugs is not high, but if we know patient’s problems, a posteriori probability can significantly increase. A split menu is an interface that shows a few likely options at the top of the menu, as an intelligent list and the complete set of options in the lower portion of the list. It is known from the literature that 4 machine learning models (the frequency model, Bayes model, linear regression model, and discriminant analysis model) have been developed for intelligent list creation. The thesis deals with the theoretical background of these models and it analyses the performances of these menus. As the method which may out-perform Bayes model, logistic regression model and a split menu based on logistic regression model and Bayes model have been developed. Performances of these models have been compared. A proposal for the follow-up in this particular research area has been made.
Keywords
Keywords (english)
Language	croatian
URN:NBN	urn:nbn:hr:168:322521
Study programme	Title: Računarstvo Study programme type: sveučilišni Study level: poslijediplomski znanstveni (magistarski) Academic / professional title: magistar inženjer računarstva (magistar inženjer računarstva)
Type of resource	text
File origin	Born digital
Access conditions	Closed access
Terms of use
Repository	FER Repository
Created on	2019-04-04 08:04:05