Accepted for/Published in: JMIR Medical Education
Date Submitted: Sep 1, 2020
Date Accepted: Aug 8, 2021
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Medical Data Mining Course Development in Postgraduate Medical Education: Integrating Student Perceptions and New Internet Technologies
ABSTRACT
Background:
Postgraduate medical students' demand for data capabilities is growing, as biomedical research becomes more data-driven, integrative and computational. In context of the application of big data in health and medicine, how to integrate data science skills into postgraduate medical education becomes important.
Objective:
This study aimed to demonstrate the design and implementation of a medical data mining course for targeted postgraduate students in a medical school in China.
Methods:
We developed a data mining course, "Practical Techniques of Medical Data Mining", and completed its online teaching for postgraduate medical education at Peking Union Medical College. To clarify the needs and preferences of targeted learners, we conducted a Web-based questionnaire survey. Three technical platforms (Rain Classroom, Tencent Meeting and WeChat) were chosen for online teaching. We also developed a medical data mining platform called “MedHub” to address problems of the data mining algorithm demonstration and learning time constraints.
Results:
Totally, 200 postgraduate medical students covering 30 different professional backgrounds participated in the survey. Based on the analysis of students’ needs and expectations, we designed an optimized course structured into nine logical teaching units with four hours/unit. The course covered the contents of R programming, machine learning modelling, clinical data mining, omics data mining and etc. Each teaching unit was divided into theoretical teaching and practical demonstration. Finally, this nine-week course was successfully implemented in an online format from May to July in the spring semester of 2020. A total of 6 faculty members and 317 students participated in the course.
Conclusions:
It is effective to integrate student perceptions and online course formats for data science course development. The diverse course content and online educational activities can help students translate theoretical knowledge into necessary data science skills, thereby benefiting more postgraduate medical studies.
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