Machine Learning Method in Medical Education: Focusing on Research Case of Press Frame on Asbestos
- Author:
Junhewk KIM
1
;
So Yun HEO
;
Shin Ik KANG
;
Geon Il KIM
;
Dongmug KANG
Author Information
1. Department of Medical Ethics and Health Policy, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA.
- Publication Type:Original Article
- Keywords:
Asbestos;
Structured topic modelling;
Automated text analysis;
Machine learning;
Big data
- MeSH:
Asbestos;
Education, Medical;
Humans;
Korea;
Machine Learning;
Methods;
Social Problems;
Social Responsibility
- From:
Korean Medical Education Review
2017;19(3):158-168
- CountryRepublic of Korea
- Language:Korean
-
Abstract:
There is a more urgent call for educational methods of machine learning in medical education, and therefore, new approaches of teaching and researching machine learning in medicine are needed. This paper presents a case using machine learning through text analysis. Topic modeling of news articles with the keyword ‘asbestos’ were examined. Two hypotheses were tested using this method, and the process of machine learning of texts is illustrated through this example. Using an automated text analysis method, all the news articles published from January 1, 1990 to November 15, 2016 in South Korea which included ‘asbestos’ in the title and the body were collected by web scraping. Differences in topics were analyzed by structured topic modelling (STM) and compared by press companies and periods. More articles were found in liberal media outlets. Differences were found in the number and types of topics in the articles according to the partisanship and period. STM showed that the conservative press views asbestos as a personal problem, while the progressive press views asbestos as a social problem. A divergence in the perspective for emphasizing the issues of asbestos between the conservative press and progressive press was also found. Social perspective influences the main topics of news stories. Thus, the patients' uneasiness and pain are not presented by both sources of media. In addition, topics differ between news media sources based on partisanship, and therefore cause divergence in readers' framing. The method of text analysis and its strengths and weaknesses are explained, and an application for the teaching and researching of machine learning in medical education using the methodology of text analysis is considered. An educational method of machine learning in medical education is urgent for future generations.
