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scientific master's theses
Inteligentno sučelje za unos medicinskih podataka
Šimunić, Krešimir (2000)
University of Zagreb
Faculty of Electrical Engineering and Computing
Department of Applied Mathematics

Cite this document

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

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

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

Šimunić, K. (2000). 'Inteligentno sučelje za unos medicinskih podataka', Scientific master's theses, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 14 July 2020, https://urn.nsk.hr/urn:nbn:hr:168:322521

Šimunić K. Inteligentno sučelje za unos medicinskih podataka [Scientific master's theses]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2000 [cited 2020 July 14] Available at: https://urn.nsk.hr/urn:nbn:hr:168:322521

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

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Metadata
TitleInteligentno sučelje za unos medicinskih podataka
Title (english)Intelligent Interface for Medical Data Entry
AuthorKrešimir Šimunić
MentorDamir Kalpić (mentor)
Committee memberVedran Mornar (član povjerenstva)
Committee memberDamir Kalpić (član povjerenstva)
Committee memberVlatko Čerić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
(Department of Applied Mathematics)
Zagreb
Defense date and country2000-06-07, 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)
Languagecroatian
URN:NBNurn:nbn:hr:168:322521
Study programmeTitle: Postgraduate master programe in computing
Study programme type: university
Study level: postgraduate
Academic / professional title: magistar inženjer računarstva (magistar inženjer računarstva)
Type of resourceText
File originBorn digital
Access conditionsClosed access
Terms of useLicence In copyright icon
RepositoryFER Repository
Created on2019-04-04 08:04:05

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