Finding overlapping terms in medical and health care curriculum
using text mining methods: rehabilitation representation – a
proof of concept

J 2017

Finding overlapping terms in medical and health care curriculum using text mining methods: rehabilitation representation – a proof of concept

KAROLYI, Matěj, Martin KOMENDA, Radka JANOUŠOVÁ, Martin VÍTA, Daniel SCHWARZ et. al.

Základní údaje

Originální název

Finding overlapping terms in medical and health care curriculum using text mining methods: rehabilitation representation – a proof of concept

Autoři

KAROLYI, Matěj, Martin KOMENDA, Radka JANOUŠOVÁ, Martin VÍTA a Daniel SCHWARZ

Vydání

MEFANET Journal, Brno, Facta Medica, 2017, 1805-9163

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

50300 5.3 Education

Stát vydavatele

Česká republika

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Organizační jednotka

Lékařská fakulta

Klíčová slova anglicky

natural language processing; curriculum; education; medical; rehabilitation

Štítky

curriculum, Education, natural language processing, OPTIMED, rehabilitation

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 24. 1. 2017 09:54, Mgr. Matěj Karolyi

Anotace

V originále

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.

Citovat

KAROLYI, Matěj, Martin KOMENDA, Radka JANOUŠOVÁ, Martin VÍTA a 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.

@article{1369239,
   author = {Karolyi, Matěj and Komenda, Martin and Janoušová, Radka and Víta, Martin and Schwarz, Daniel},
   article_location = {Brno},
   keywords = {natural language processing; curriculum; education; medical; rehabilitation},
   language = {eng},
   issn = {1805-9163},
   journal = {MEFANET Journal},
   title = {Finding overlapping terms in medical and health care curriculum using text mining methods: rehabilitation representation – a proof of concept},
   url = {http://mj.mefanet.cz/mj-20161227},
   year = {2017}
}

TY  - JOUR
ID  - 1369239
AU  - Karolyi, Matěj - Komenda, Martin - Janoušová, Radka - Víta, Martin - Schwarz, Daniel
PY  - 2017
TI  - Finding overlapping terms in medical and health care curriculum using text mining methods: rehabilitation representation – a proof of concept
JF  - MEFANET Journal
PB  - Facta Medica
SN  - 18059163
KW  - natural language processing
KW  - curriculum
KW  - education
KW  - medical
KW  - rehabilitation
UR  - http://mj.mefanet.cz/mj-20161227
N2  - 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.
ER  -

KAROLYI, Matěj, Martin KOMENDA, Radka JANOUŠOVÁ, Martin VÍTA a Daniel SCHWARZ. Finding overlapping terms in medical and health care curriculum using text mining methods: rehabilitation representation – a proof of concept. \textit{MEFANET Journal}. Brno: Facta Medica, 2017. ISSN~1805-9163.

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