Numbering Code	G-MED26 7D008 LJ89	Year/Term	2022 ・ Year-round
Number of Credits	4	Course Type	Lecture
Target Year	1st year doctoral students	Target Student
Language	Japanese	Day/Period	Thu.1
Instructor name	ISHIZU KOUICHI (Graduate School of Medicine Associate Professor)
Outline and Purpose of the Course	Through the adoption of electronic medical records and the digitization of medical images, enormous amounts of medical data have been accumulated. The analysis of these big data using the latest data mining technology is expected to help improve diagnostic accuracy and the efficiency of medical care. From the perspective of local medicine, its application to telemedicine is also underway. We help students experience big data analysis and understand the workings of the new clinical support system.
Course Goals	・Understand class discrimination and clustering techniques in multivariate analysis to both consider and apply their medical applications toward your own research objectives. ・Understand research trends and read prior research critically. ・Acquire the skills necessary for conducting research such as transcription, logic, and structure that must be considered while writing a research paper, and the ability to pursue originality.
Schedule and Contents	Students will deepen their knowledge and understanding of issues related to medical data, data mining, diagnostic support, and telemedicine through discussions covering research methodology and implementation, as well as by writing research papers in line with their chosen themes. Seminars are held twice a month, with a series of lectures held in periods 1 and 2 on Thursdays. A total of 30 lectures will be delivered over 15 days. Sessions 1-2: Setting assignments and confirming fundamental knowledge Sessions 3-15: Collection and critical examination of prior research and scrutiny of research methods Sessions 16-29. Creation of a research plan and critical examination thereof Session 30. Summary
Evaluation Methods and Policy	Assessment of the extent of accomplishment of the given assignment. A proactive attitude toward discussions etc., will also be taken into account.
Course Requirements	None
Study outside of Class (preparation and review)	As PhD students, we especially value independence, and expect that you will engage in independent study to delve deeper into your areas of research.
Textbooks	Textbooks/References	Not used
References, etc.	Introduced during class