History
- Mar. 2014
- Department of Cancer Control and Policy
- Mar. 2017
- Department of Cancer Control and Population Health
Overview
Students in Department of Cancer Control and Population Health will be trained in a relative substantive area and be prepared to perform the national cancer control. The program is an interdisciplinary curriculum covers cancer prevention and control, cancer epidemiology and nutrition, cancer statistics and some other areas.
Graduate Program Credit Requirements (Students admitted in 2019 spring semester ~ 2021 fall semester)
Graduate Program Credit Requirements
| Division |
Core Courses |
Elective Courses |
Independent Study(IS) |
Thesis Research |
Total |
| Master’s Program |
7 |
14 |
6 |
3 |
30 |
| Doctoral Program |
- |
18 |
9 |
3 |
30 |
* Independent Study(IS) can be replaced with elective course
Department of Cancer Biomedical Science
History
- Mar. 2014
- Department of System Cancer Science
- Mar. 2017
- Department of Cancer Biomedical Science
Overview
Students in Department of Cancer Biomedical Science will be trained in a relative substantive area and be prepared to study causes of cancer and to establish a plan for cancer therapeutics. The program is an interdisciplinary curriculum covers initiative cancer science, cancer science for system biology and new therapeutics.
Graduate Program Credit Requirements (Students admitted in 2022 spring semester ~ )
Graduate Program Credit Requirements
| Division |
Core Courses |
Elective Courses |
Independent Study(IS) |
Thesis Research |
Total |
| Master’s Program |
7 |
14 |
6 |
3 |
30 |
| Doctoral Program |
- |
18 |
9 |
3 |
30 |
* Independent Study(IS) can be replaced with elective course
Department of Cancer AI & Digital Health
History
- Mar. 2023
- Department of AI & Digital Health
Overview
Students of the Department of Cancer AI & Digital Health will gain scientific knowledge and technical skills in data science, artificial intelligence, statistics, and informatics. Courses of the program will guide the students to pursue the multidisciplinary application of their knowledge and skills to big data and to perform collaborative research to solve complex questions in epidemiology, health policy and management, and biomedical science related to cancer.
Graduate Program Credit Requirements (Students admitted in 2023 spring semester ~ )
Graduate Program Credit Requirements
| Division |
Core Courses |
Elective Courses |
Independent Study(IS) |
Thesis Research |
Total |
| Master’s Program |
13 |
8 |
6 |
3 |
30 |
| Doctoral Program |
- |
18 |
9 |
3 |
30 |
* Independent Study(IS) can be replaced with elective course Graduate Program Credit Requirements
Track-realted Information
Foundation
Master’s degree
- Learning basic health statistics and analysis methods
- Grasping computer programming and AI algorithms
- Understanding structures and processing of various data types (images, signals, EMR, genomics)
Doctorate degree
- Mastering advanced statistical methods for health research
- Learning advanced programming and AI algorithms
- Applying sophisticated data processing techniques
Core Competencies
- Practical health data
management and processing
- Enhancing statistical analysis skills for
diverse healthcare data
- Developing AI capabilities
for healthcare data types
- Cultivating experts in health statistics
and AI through interdisciplinary research
- Reinforcing research with advanced AI algorithms tailored to data types
- Theoretical exploration of new AI
algorithms and statistical methods for
advanced cancer research
Specialized Fields of Entry
- Clinical trial-related divisions in
hospitals and pharmaceutics
- Big data-related departments in
government agencies
- Health statistics departments in
research institutes
- Medical AI research and solution
startups and ventures
- Clinical trial agencies
(hospitals and pharma)
- Health statistics and AI-related
departments in government
- Medical AI research and solution
startups and ventures
- Experts in health informatics for
precision medicine in cancer
A Course of Study
암AI디지털헬스학과 석사/박사 과목 안내
| |
Master’s program |
|
Doctoral program |
| Core class |
- Deep learning algorithms
- Principles of biostatistics
- Regression method in biostatistics
|
Elective
course |
Statistics |
- Introduction to statistical computing
- Modern health data science
- Survival data analysis
- Spatial data analysis
- Introduction to statistics in genetics
|
 |
- Health survey design and analysis
- Advanced statistical methods and modeling
- Systems epidemiology
|
Artificial
Intelligence |
- Computer programming language for A.I.
- Basic mathematics for data science
- Data processing skills
- Machine learning algorithms
|
- Advanced data science skills
- Advanced AI algorithms
|
Core or
Elective (Basic)1st semester
Elective (Core)2~3rd semester
Field Research4~6th semester
Field Research (Core)
Preparation for thesis7th semester
Thesis8th semester
