Učni načrt predmeta

Predmet:
Zdravstveni ekspertni sistemi na daljavo
Course:
Expert Systems for eHealth
Študijski program in stopnja /
Study programme and level		 Študijska smer /
Study field		 Letnik /
Academic year		 Semester /
Semester
Informacijske in komunikacijske tehnologije, 3. stopnja Računalniške strukture in sistemi 1 1
Information and Communication Technologies, 3rd cycle Computer Structures and Systems 1 1
Vrsta predmeta / Course type
Izbirni / Elective
Univerzitetna koda predmeta / University course code:
IKT3-909
Predavanja
Lectures		 Seminar
Seminar		 Vaje
Tutorial		 Klinične vaje
work		 Druge oblike
študija		 Samost. delo
Individ. work		 ECTS
15 15 15 105 5

*Navedena porazdelitev ur velja, če je vpisanih vsaj 15 študentov. Drugače se obseg izvedbe kontaktnih ur sorazmerno zmanjša in prenese v samostojno delo. / This distribution of hours is valid if at least 15 students are enrolled. Otherwise the contact hours are linearly reduced and transfered to individual work.

Nosilec predmeta / Course leader:
prof. dr. Barbara Koroušić Seljak
Sodelavci / Lecturers:
doc. dr. Tome Eftimov
Jeziki / Languages:
Predavanja / Lectures:
Slovenščina, angleščina / Slovenian, English
Vaje / Tutorial:
Pogoji za vključitev v delo oz. za opravljanje študijskih obveznosti:
Prerequisites:

Zaključen študij druge stopnje s področja informacijskih ali komunikacijskih tehnologij ali zaključen študij druge stopnje na drugih področjih z znanjem osnov s področja predmeta. Potrebna so tudi osnovna znanja matematike, računalništva in informatike.

Completed second cycle studies in information or communication technologies or completed second cycle studies in other fields with knowledge of fundamentals in the field of this course. Basic knowledge of mathematics, computer science and informatics is also requested.

Vsebina:
Content (Syllabus outline):

Znanstvena metoda:
strukture znanstvenega védenja, znanstvene aktivnosti in procesi

Računalniška podpora zdravstvu:
uvod, zgodovinski razvoj področja

Modeliranje sistemov e-zdravje / m-zdravje:
metodologije in metode za analizo in načrtovanje sistemov e-zdravje / m-zdravje; osnove načrtovanja; sodobni postopki za modeliranje sistemov

Napredne računalniške metode:
zajem, obdelava in analiza podatkov ter znanja,
s pomočjo statističnih metod, naravne obdelave besedil, rudarjenjem podatkov in strojnega učenja

Izvedba sistemov e-zdravje / m-zdravje:
personalizirani uporabniški vmesniki, spletni portali in aplikacije, mobilne aplikacije, baze podatkov in grafi znanja, varnostni in etični vidiki, analiza pristranskosti sistemov eZdravja

Integracija sistemov e-zdravje / m-zdravje v okolje:
povezovanje in harmonizacija podatkov, metode za testiranje in validacijo

Scientific Method:
scientific knowledge structures, scientific activities / processes

Computational Scientific Discovery:
introduction, history of development of the area

Modeling of e-health and m-health systems:
software analysis and design – methods and methodologies; design basics; modern system modeling techniques

Advanced computer methods:
capturing, processing and analysis of data and information, using statistical methods, natural language processing, data mining and machine learning

Implementation of e-health and m-health systems:
dashboards, web-portals and applications, mobile apps, databases and knowledge graphs, security and ethical aspects, bias analysis of eHealth systems

Integration of e-health and m-health systems in the environment:
data linkage and harmonisation, methods for testing and validation

Temeljna literatura in viri / Readings:

Izbrana poglavja iz naslednjih knjig: / Selected chapters from the following books:
- J. Alammar & M. Grootendorst (2024) Hands-on large language models: Language understanding and generation. O’Reilly Media.
- E. Topol, The Creative Destruction of Medicine: How the Digital Revolution Will Create Better Health Care. Basic Books, 2012. ISBN 978-0-465-02550-3.
- A. Blum, J. Hopcroft, R. Kannan; Foundations of Data Science. Cambridge University Press, 2020. ISBN: 978-1-108-48506-7.
- D. Fensel, U. Şimşek, K. Angele, E. Huaman, E., Kärle, O. Panasiuk, I. Toma, J. Umbrich, A. Wahler; Knowledge Graphs. Springer International Publishing, 2020. ISBN: 978-3-030-37439-6.
- E. Alpaydin; Introduction to machine learning. MIT press, 2020. ISBN: 978-0-262-02818-9.

Cilji in kompetence:
Objectives and competences:

Cilj predmeta je seznaniti študenta s področjem sistemov e-zdravje in m-zdravje.

Kompetence študenta z uspešno zaključenim predmetom bodo vključevale razumevanje osnovnih pojmov iz področja, poznavanje sodobnih metod in znanje o primerih uporabe le-teh na vse bolj pomembnem znanstvenem področju (znanosti o klinični prehrani).

The goal of the course is to familiarize the student with e-health and m-health systems.

The competencies of the students completing this course successfully would include understanding of basic concepts from both areas, familiarity with state-of-the art methods, and knowledge of examples applications from the advancing scientific field (clinical nutrition).

Predvideni študijski rezultati:
Intendeded learning outcomes:

Študenti bodo z uspešno opravljenimi obveznostmi tega predmeta pridobili:
- strukture znanstvenega védenja, kot so znanstvene aktivnosti in procesi
- razumevanje konceptov modeliranja sistemov e-zdravje / m-zdravje
- pregled obstoječih nalog in metod za zajem, obdelavo in analizo podatkov ter znanja
- pregled obstoječih nalog in metod za izvedbo sistemov e-zdravje / m-zdravje
- pregled obstoječih nalog in metod za integracijo sistemov e-zdravje / m-zdravje v okolje
- sposobnost uporabe obstoječih metod na novih sistemih e-zdravje / m-zdravje
- sposobnost ugotavljanja primernosti metod za snovanje, izvedbo in validacijo sistemov e-zdravje / m-zdravje

Students successfully completing this course will acquire:
- Scientific knowledge structures, such as activities / processes
- Understanding concepts of modelling e-health / m-health systems
- Overview of existing tasks and methods for data and knowledge capturing, processing and analysis
- Overview of existing tasks and methods for implementing e-health / m-health systems
- Overview of existing tasks and methods for embedding e-health / m-health systems into external systems
- The ability to apply existing methods to new e-health / m-health systems
- The ability to identify whether methods for designing, implementation and validation of e-health / m-health systems are approapriate

Metode poučevanja in učenja:
Learning and teaching methods:

Predavanja, seminar, konzultacije, individualno delo

Lectures, seminar, consultancy, individual work

Načini ocenjevanja:
Delež v % / Weight in %
Assesment:
Seminarska naloga
50
Seminar work
Ustni zagovor seminarske naloge
50
Oral defense of seminar work
Reference nosilca / Lecturer's references:
1. A. Gjorgjevikj, M. Martinc, G. Cenikj, J. Drole, N. Ogrinc, S. Džeroski, B. Koroušić Seljak, T. Eftimov, et al. Large language models in food and nutrition science : opportunities, challenges, and the case of FoodyLLM. Current research in food science. 2026, vol. 12, [article no.] 101351, pp. 1-26, ilustr. ISSN 2665-9271. DOI: 10.1016/j.crfs.2026.101351.
2. T. Eftimov, G. Popovski, M. Petković, B. Koroušić Seljak, D. Kocev. COVID-19 pandemic changes the food consumption patterns. Trends in food science & technology, 2020, vol. 104, str. 268-272. ISSN 0924-2244. DOI: 10.1016/j.tifs.2020.08.017.
3. A. Gjorgjevikj, A. Nikolikj, B. Koroušić Seljak, T. Eftimov. User-defined trade-offs in LLM benchmarking: balancing accuracy, scale, and sustainability. Knowledge-based systems. [Online ed.]. Nov. 2025, vol. 330, pt. a, [article no.] 114405, str. 1-30, ilustr. DOI: 10.1016/j.knosys.2025.114405.
4. G. Cenikj, E. Valenčič, G. Ispirova, M. Ogrinc, R. Stojanov, P. Korošec, E. Cavallo, B. Koroušić Seljak, T. Eftimov. CafeteriaSA corpus: scientific abstracts annotated across different food semantic resources. Database. 2022, vol. 2022, str. 1-12, ilustr. ISSN 1758-0463. DOI: 10.1093/database/baac107.
5. S. Mezgec, T. Eftimov, T. Bucher, B. Koroušić Seljak. Mixed deep learning and natural language processing method for fake-food image recognition and standardization to help automated dietary assessment. Public health nutrition 22(7) 1193-1202, 2019