Study program / study programs: Advanced data analytics
Course: Data analysis in fundamental and clinical medicine
Teacher(s): Prof. Dr Biljana Miličić, Ass. Dr Jovana Kuzmanović Pfićer, Dr Tijana Išić Denčić, Prof. Dr Igor Pantić, Prof. Dr Nataša Milić, Prof. Dr Ivan Soldatović
Course status: Elective
ECTS points: 7
Prerequisites: none
Course objective:
Adopting new knowledge and skills related to data analytics in medicine.
Learning outcomes:
Training students for data processing in fundamental and clinical medical disciplines. Students will master analysis of data obtained by processing signals in the field of electrocardiography, electroencephalography, microscopy and other methods used in modern medical diagnostics. Students will also be familiarized with the basics of medical statistics, with particular reference to statistical analytical tests in medical research and computer methods for statistical processing of medical data. Within this course, students will also gain basic knowledge of medical informatics.
Course structure and content:
1. Introduction to data analysis in medicine
2. Analysis of data obtained as result of the application of diagnostic tests in medicine
3. Analysis of electrocardiograms
4. Analysis of electroencephalograms
5. Data analysis in microscopy
6. Statistical analysis in clinical medicine
7. Fundamentals of medical informatics
8. Contemporary computer programs for statistical analysis of data in medical research
9. EDC (Electronic Data Capture) systems in medicine
10. Statistical analysis in dentistry – design of repeated measurements (split-mouth design)
Literature/Readings:
Charan Singh Rayat. Statistical Methods in Medical Research. Springer, 2018, New York
Katherine Marconi, Harold Lehmann. Big Data and Health Analytics. Auerbach Publications; 1st Edition. 2014. New York
Nadinia A. Davis, Betsy J. Shiland. Statistics & Data Analytics for Health Data Management. Saunders; 1 edition. 2016. London, UK
Kim JS, Dailey R. Biostatistics for Oral Healthcare. Blackwell Pub Professional, Iowa УСА: State University Press; 2007.
The number of class hours per week:
Lectures: 5
Labs: 0
Workshops: 1
Research study: 2
Other classes: 0
Teaching methods:
Individual and group work; lectures and labs
Evaluation/Grading (maximum 100 points):
Pre-exam requirements (Project, conceptual solution): 30
Project (implementation): 70