| KOMENDA, Martin, Jakub ŠČAVNICKÝ, Petra RŮŽIČKOVÁ, Matěj KAROLYI, Petr ŠTOURAČ and Daniel SCHWARZ. Similarity detection between virtual patients and medical curriculum using R. In John Mantas, Zdenko Sonicki, Mihaela Crişan-Vida, Kristina Fišter, Maria Hägglund, Aikaterini Kolokathi, Mira Hercigonja-Szekeres. Studies in Health Technology and Informatics 255. Amsterdam: IOS Press, 2018, p. 222-226. ISBN 978-1-61499-920-1. Available from: https://dx.doi.org/10.3233/978-1-61499-921-8-222. |
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@inproceedings{1465456,
author = {Komenda, Martin and Ščavnický, Jakub and Růžičková, Petra and Karolyi, Matěj and Štourač, Petr and Schwarz, Daniel},
address = {Amsterdam},
booktitle = {Studies in Health Technology and Informatics 255},
doi = {http://dx.doi.org/10.3233/978-1-61499-921-8-222},
editor = {John Mantas, Zdenko Sonicki, Mihaela Crişan-Vida, Kristina Fišter, Maria Hägglund, Aikaterini Kolokathi, Mira Hercigonja-Szekeres},
keywords = {OPTIMED; R programming language; akutne.cz; medical curriculum; text similarity; virtual patient},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Amsterdam},
isbn = {978-1-61499-920-1},
pages = {222-226},
publisher = {IOS Press},
title = {Similarity detection between virtual patients and medical curriculum using R},
url = {http://ebooks.iospress.nl/volumearticle/50507},
year = {2018}
}
TY - JOUR
ID - 1465456
AU - Komenda, Martin - Ščavnický, Jakub - Růžičková, Petra - Karolyi, Matěj - Štourač, Petr - Schwarz, Daniel
PY - 2018
TI - Similarity detection between virtual patients and medical curriculum using R
PB - IOS Press
CY - Amsterdam
SN - 9781614999201
KW - OPTIMED
KW - R programming language
KW - akutne.cz
KW - medical curriculum
KW - text similarity
KW - virtual patient
UR - http://ebooks.iospress.nl/volumearticle/50507
L2 - http://ebooks.iospress.nl/volumearticle/50507
N2 - This paper presents the domain of information sciences, applied informatics and biomedical engineering, proposing to develop methods for an automated detection of similarities between two particular virtual learning environments - virtual patients at Akutne.cz and the OPTIMED curriculum management system - in order to provide support to clinically oriented stages of medical and healthcare studies. For this purpose, the authors used large amounts of text-based data collected by the system for mapping medical curricula and through the system for virtual patient authoring and delivery. The proposed text-mining algorithm for an automated detection of links between content entities of these systems has been successfully implemented by the means of a web-based toolbox.
ER -
KOMENDA, Martin, Jakub ŠČAVNICKÝ, Petra RŮŽIČKOVÁ, Matěj KAROLYI, Petr ŠTOURAČ and Daniel SCHWARZ. Similarity detection between virtual patients and medical curriculum using R. In John Mantas, Zdenko Sonicki, Mihaela Cri\c san-Vida, Kristina Fišter, Maria Hägglund, Aikaterini Kolokathi, Mira Hercigonja-Szekeres. \textit{Studies in Health Technology and Informatics 255}. Amsterdam: IOS Press, 2018, p.~222-226. ISBN~978-1-61499-920-1. Available from: https://dx.doi.org/10.3233/978-1-61499-921-8-222.
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