KAROLYI, Matěj, Martin KOMENDA, Radka JANOUŠOVÁ, Martin VÍTA and Daniel SCHWARZ. Finding overlapping terms in medical and health care curriculum using text mining methods: rehabilitation representation – a proof of concept. MEFANET Journal. Brno: Facta Medica, 2017. ISSN 1805-9163.
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Basic information
Original name Finding overlapping terms in medical and health care curriculum using text mining methods: rehabilitation representation – a proof of concept
Authors KAROLYI, Matěj, Martin KOMENDA, Radka JANOUŠOVÁ, Martin VÍTA and Daniel SCHWARZ.
Edition MEFANET Journal, Brno, Facta Medica, 2017, 1805-9163.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 50300 5.3 Education
Country of publisher Czech Republic
Confidentiality degree is not subject to a state or trade secret
WWW URL
Organization unit Faculty of Medicine
Keywords in English natural language processing; curriculum; education; medical; rehabilitation
Tags curriculum, Education, natural language processing, OPTIMED, rehabilitation
Tags International impact, Reviewed
Changed by Changed by: Mgr. Matěj Karolyi, učo 117307. Changed: 24/1/2017 09:54.
Abstract
Background: Various institutions dealing with higher medical and healthcare education have different methods of organising their study programmes, which typically involve hundreds of theoretically and clinically focused courses. The importance of a well-balanced curriculum is indisputable – the society needs qualified doctors because people’s health is necessary for the functioning and development of the entire society. Objectives: In this paper, we introduce our innovative approach to identify overlaps among medical or healthcare disciplines using term similarity. A close attention is focused on the discipline of Rehabilitation and Physical Medicine and its role in the General Medicine study field in the Faculty of Medicine at Masaryk University. Methods: Data and text mining techniques were used in practice, in accordance with a time-tested methodological background, which systematically covers all fundamental steps to discover and to extract knowledge from data repositories. In order to extract term similarities from a medical curriculum dataset, the CRISP-DM reference model was chosen as a well-documented practical guideline. Results: The achieved results clearly demonstrate overlapping areas among the defined disciplines in the explored curriculum. The resulting comprehensive analytical report presents the term occurrence in a set of figures and tables, which were thoroughly evaluated by experts familiar with the curriculum design process. Conclusions: In this case study, we have proposed an innovative method for identifying overlaps of terms occurring in medical and healthcare disciplines when compared to the discipline of Rehabilitation and Physical Medicine. The first results are promising in the sense of face validity. We believe that this approach can be used similarly to gain an objective overview of the entire curriculum.
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