Web-Weka Meets D3.js in Web Based Medical Data Mining

2015 ◽  
Author(s):  
Bin Liu ◽  
YouSong Peng ◽  
YuanQiang Zou ◽  
JianHong Wang ◽  
TaiJiao Jiang
Keyword(s):  
Data Mining ◽  
Medical Data ◽  
Medical Data Mining ◽  
Web Based

scholarly journals Medical Data Mining Course Development in Postgraduate Medical Education: Web-Based Survey and Case Study (Preprint)

2020 ◽  
Author(s):  
Lin Yang ◽  
Si Zheng ◽  
Xiaowei Xu ◽  
Yueping Sun ◽  
Xuwen Wang ◽  
...  
Keyword(s):  
Data Mining ◽  
Medical Education ◽  
Medical Students ◽  
Postgraduate Medical Education ◽  
Medical Data ◽  
Medical Data Mining ◽  
Web Based ◽  
Wide Range ◽  
R Programming

BACKGROUND Medical postgraduates’ demand for data capabilities is growing, as biomedical research becomes more data driven, integrative, and computational. In the context of the application of big data in health and medicine, the integration of data mining skills into postgraduate medical education becomes important. OBJECTIVE This study aimed to demonstrate the design and implementation of a medical data mining course for medical postgraduates with diverse backgrounds in a medical school. METHODS We developed a medical data mining course called “Practical Techniques of Medical Data Mining” for postgraduate medical education and taught the course online at Peking Union Medical College (PUMC). To identify the background knowledge, programming skills, and expectations of targeted learners, we conducted a web-based questionnaire survey. After determining the instructional methods to be used in the course, three technical platforms—Rain Classroom, Tencent Meeting, and WeChat—were chosen for online teaching. A medical data mining platform called Medical Data Mining - R Programming Hub (MedHub) was developed for self-learning, which could support the development and comprehensive testing of data mining algorithms. Finally, we carried out a postcourse survey and a case study to demonstrate that our online course could accommodate a diverse group of medical students with a wide range of academic backgrounds and programming experience. RESULTS In total, 200 postgraduates from 30 disciplines participated in the precourse survey. Based on the analysis of students’ characteristics and expectations, we designed an optimized course structured into nine logical teaching units (one 4-hour unit per week for 9 weeks). The course covered basic knowledge of R programming, machine learning models, clinical data mining, and omics data mining, among other topics, as well as diversified health care analysis scenarios. Finally, this 9-week course was successfully implemented in an online format from May to July in the spring semester of 2020 at PUMC. A total of 6 faculty members and 317 students participated in the course. Postcourse survey data showed that our course was considered to be very practical (83/83, 100% indicated “very positive” or “positive”), and MedHub received the best feedback, both in function (80/83, 96% chose “satisfied”) and teaching effect (80/83, 96% chose “satisfied”). The case study showed that our course was able to fill the gap between student expectations and learning outcomes. CONCLUSIONS We developed content for a data mining course, with online instructional methods to accommodate the diversified characteristics of students. Our optimized course could improve the data mining skills of medical students with a wide range of academic backgrounds and programming experience.

Uniqueness of medical data mining

2002 ◽  
Vol 26 (1-2) ◽  
pp. 1-24 ◽  
Author(s):  
Krzysztof J. Cios ◽  
G. William Moore
Keyword(s):  
Data Mining ◽  
Medical Data ◽  
Medical Data Mining

scholarly journals A bibliometric analysis and visualization of medical data mining research

Medicine ◽  
2020 ◽  
Vol 99 (22) ◽  
pp. e20338 ◽  
Author(s):  
Yuanzhang Hu ◽  
Zeyun Yu ◽  
Xiaoen Cheng ◽  
Yue Luo ◽  
Chuanbiao Wen
Keyword(s):  
Data Mining ◽  
Bibliometric Analysis ◽  
Medical Data ◽  
Medical Data Mining

Medical data mining: insights from winning two competitions

2009 ◽  
Vol 20 (3) ◽  
pp. 439-468 ◽  
Author(s):  
Saharon Rosset ◽  
Claudia Perlich ◽  
Grzergorz Świrszcz ◽  
Prem Melville ◽  
Yan Liu
Keyword(s):  
Data Mining ◽  
Medical Data ◽  
Medical Data Mining
Sign in / Sign up

Export Citation Format

Share Document