Data Analytics and Artificial Intelligence in Healthcare (VVDG_040)
About Study Course
Objective
The aim of the study course is to introduce the basic principles of big data analysis, data visualization, artificial intelligence and machine learning in order to successfully use these skills for healthcare improvement and innovation. The course will provide an opportunity to achieve a high level of digital skills to function effectively in a digital healthcare context.
Prerequisites
- Understanding the importance and role of information technology and health data in improving healthcare and creating innovations;
- An idea of related legislation relating to the processing and privacy of health data;
- Basic skills in working with data (searching for information, processing data with Microsoft Excel or equivalent application software).
Learning outcomes
- Know descriptive and prognostic health data analysis methods;
- Know and characterize the approaches and possibilities of health data visualization;
- Know different artificial intelligence solutions and their application in health care;
- Familiarize and distinguish the types of machine learning and describe their application possibilities in health care;
- To distinguish between the types of machine learning and their applications in healthcare and the ways in which they can be applied in healthcare.
- Argue and integrate descriptive and prognostic health data analysis methods;
- Apply health data visualization approaches and methods for data-based decision-making;
- Choose appropriate solutions and identify requirements for the generation, selection and further analytical processing of big data using a high-performance viewing approach;
- Understand and choose the most suitable artificial intelligence solution in the implementation of certain healthcare processes;
- Identify opportunities for machine learning applications in healthcare.
- Identify, select and apply analytical approaches of health big data in data-based decision-making;
- Improve existing health care technological solutions using artificial intelligence and machine learning approaches;
- Create data-based healthcare solutions and innovations;
- Implement a machine learning approach in solving health efficiency and problem issues.
Study course planning
| Study programme | Study semester | Program level | Study course category | Lecturers | Schedule |
|---|---|---|---|---|---|
| Pharmacy, FF | 8 | Master’s | Limited choice | Daiga Behmane, Didzis Rūtītis, Uģis Kārlis Sprūdžs, Oskars Radziņš, Sergio Andres Uribe Espinoza, Jevgenijs Proskurins | |
| Pharmacy, FF | 10 | Master’s | Limited choice | Daiga Behmane, Didzis Rūtītis, Uģis Kārlis Sprūdžs, Oskars Radziņš, Sergio Andres Uribe Espinoza, Jevgenijs Proskurins | |
| Rehabilitation, REHM | 4 | Master’s | Limited choice | Didzis Rūtītis, Uģis Kārlis Sprūdžs, Oskars Radziņš, Sergio Andres Uribe Espinoza, Jevgenijs Proskurins | |
| Industrial pharmacy, FFRf | 2 | Master’s | Limited choice | Daiga Behmane, Didzis Rūtītis, Uģis Kārlis Sprūdžs, Oskars Radziņš, Sergio Andres Uribe Espinoza, Jevgenijs Proskurins | |
| Public Health, SVFM | 2 | Master’s | Limited choice | Daiga Behmane, Didzis Rūtītis, Uģis Kārlis Sprūdžs, Oskars Radziņš, Sergio Andres Uribe Espinoza, Jevgenijs Proskurins | |
| Law Science, TZMjvpz | 3 | Master’s | Limited choice | Daiga Behmane, Didzis Rūtītis, Uģis Kārlis Sprūdžs, Oskars Radziņš, Sergio Andres Uribe Espinoza, Jevgenijs Proskurins | |
| Law Science, TZMp | 2 | Master’s | Limited choice | Daiga Behmane, Didzis Rūtītis, Uģis Kārlis Sprūdžs, Oskars Radziņš, Sergio Andres Uribe Espinoza, Jevgenijs Proskurins |
| Study programme | Study semester | Program level | Study course category | Lecturers | Schedule |
|---|---|---|---|---|---|
| Biostatistics, MFBS | 3 | Master’s | Limited choice | ||
| Pharmacy, FF | 7 | Master’s | Limited choice | ||
| Public Health, SVFM | 1 | Master’s | Limited choice |
