Knowledge discovery, prediction and prognosis using biomedical data

1.

Subject title

Knowledge discovery, prediction and prognosis using biomedical data

Откривање на знаење, предвидување и прогноза користејќи биомедицински податоци

2.

Code

m23_s_058

3.

Study program

IT management, Bioinformatics, Security, Cryptography and Coding, Еducation with ICT, Eco-informatics, Inteligent Systems, Computer Science, Statistics and Data Analytics, Software for embedded systems, Cloud Computing, IT management, Bioinformatics, Security, Cryptography and Coding, Cloud Computing, Data science in computer science and engineering, Internet Technologies and cyber security, Statistics and Data Analytics, Software Engineering, Software Engineering,

4.

Organizer of the study program (unit, institute, department, division)

Faculty of Information Sciences and Computer Engineering

5.

Study cycle (first, second, third)

Втор циклус

6.

Academic year / semester

5 / Летен

7. Number of ECTS credits

6.0

8.

Instructor

проф. д-р Дејан Ѓорѓевиќ проф. д-р Ѓорѓи Маџаров ворн. проф. д-р Иван Китановски проф. д-р Ивица Димитровски ворн. проф. д-р Катарина Тројачанец Динева проф. д-р Сузана Лошковска

9.

Prerequisites for enrollment

10.

Subject goals and competencies:

Advances in medicine and genetics, as well as the rapid evolution of technology, continuously increase the amount of generated biomedical data in the clinical and research centers. Variety of data types are produced, including medical images, genetic markers, cognitive tests results, blood results etc. They all need to be efficiently organized, stored, analyzed, and represented in order to provide easier and more appropriate access, and to enable use of those data for knowledge discovery, prediction and prognosis. This could support and even, improve the diagnostic and therapeutic processes. The rapid development in the field of machine learning is having a profound impact in biomedical domain, where the great variety of tasks and data types open space for applying machine learning algorithms and using their benefits. Тhe course aims to give students the ability to pose and answer meaningful clinical questions, use the most relevant methods and applications of machine learning in biomedicine and healthcare, (large-scale) data manipulation and analysis, annotation and indexing of unstructured content, clinically relevant knowledge extraction, intelligent feature engineering, identify what data and algorithms provide best prediction and/or prognosis, support the diagnostics and therapeutic treatments in different ways.

11.

Subject content:

Clinical data organization and usage. Strategies for knowledge discovery from medical observations. Medical images - as a fundamental part of many medical disciplines. Algorithms and strategies based on and appropriate for medical images, challenges and possible solutions. Temporal time series of structured data. Extracting clinically powerful knowledge from structured medical time series, coping with challenges, analyzing and finding possible solutions. Missing data. Defining risk factors and the information usage to develop recommendations for prevention. Diagnosis and prognosis - finding diagnostic biomarkers and biomarkers for prognosis. Treatment reaction analysis.

12.

Learning methods:

Предавања поддржани со презентации преку слајдови, интерактивни предавања, вежби (користење на опрема и софтверски пакети), тимска работа, пример случаи, поканети гости предавачи, самостојна изработка и одбрана на проектна задача и семинарска работа, учење во електронско опкружување (форуми, консултации).

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

60 + 0 + 45 + 45 + 30 = 180 hours

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

60 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

0 hours

16.

Other forms of activities

16.1.

Project tasks

45 hours

16.2.

Independent tasks

45 hours

16.3.

Homework

30 hours

17.

Grading method

17.1.

Tests

15 points

17.2.

Seminar work / project (presentation: written and oral)

45 points

17.3.

Activities and learning

15 points

17.4.

Final exam

0 points

18.

Grading criteria (points / grade)

up to 50 points

5 (five) (F)

from 51 to 60 points

6 (six) (E)

from 61 to 70 points

7 (seven) (D)

from 71 to 80 points

8 (eight) (C)

from 81 to 90 points

9 (nine) (B)

from 91 to 100 points

10 (ten) (A)

19.

Condition for signature and taking final exam

NULL

20.

Language of instruction

македонски и англиски

21.

Quality assurance method

Механизам на интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

22.2.

Additional literature