Veidlapa Nr. M-3 (8)
Study Course Description
Data Analytics and Artificial Intelligence in Healthcare
Course Code
VVDG_040
Branch of Science
Other social sciences
ECTS
3.00
Target Audience
Business Management; Health Management; Management Science; Pharmacy
LQF
Level 7
Study Type And Form
Full-Time
Study Course Implementer
Course Supervisor
Uģis Kārlis Sprūdžs
Structure Unit Manager
Daiga Behmane
Structural Unit
Health Management Teaching Group
Contacts
Dzirciema street 16, Rīga
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.
Preliminary Knowledge
- 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
Knowledge
1.- 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.
Skills
1.- 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.
Competences
1.- 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.
Assessment
Individual work
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Title
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% from total grade
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Grade
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1.
Individual work |
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Acquisition of materials placed in e-studies (video lectures, articles, publications, datasets), self-testing tasks.
Development of independent work: to perform research-based data analysis, visualization and prognostic model development for a specific health dataset with the data analytics and prognostics tools offered in the course. In order to assess the quality of the study course as a whole, the student must fill in the study course evaluation questionnaire on the Student Portal.
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Examination
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Title
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% from total grade
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Grade
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1.
Examination |
-
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-
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Study course assessment: final exam test assessment. The exam is available to students who have successfully completed the test on all course topics.
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Study Course Theme Plan
FULL-TIME
Part 1
-
Lecture
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Modality
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Location
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Contact hours
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On site
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Auditorium
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2
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Topics
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Use of data analytic software and data visualization in exploratory data anlysis
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-
Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Use of data analytic software and data visualization in exploratory data anlysis
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-
Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Computer room
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2
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Topics
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Use of data analytic software and data visualization in exploratory data anlysis
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-
Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Application of artificial intelligence in health care
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Application of artificial intelligence in health care
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-
Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Computer room
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2
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Topics
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Application of artificial intelligence in health care
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-
Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Machine learning applications for predictive analysis in healthcare
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-
Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Computer room
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2
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Topics
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Machine learning applications for predictive analysis in healthcare
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-
Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Computer room
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2
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Topics
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Machine learning applications for predictive analysis in healthcare
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-
Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Deep learning applications for predictive analysis in healthcare
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Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Computer room
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2
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Topics
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Deep learning applications for predictive analysis in healthcare
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Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Computer room
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2
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Topics
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Deep learning applications for predictive analysis in healthcare
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Total ECTS (Creditpoints):
3.00
Contact hours:
24 Academic Hours
Final Examination:
Exam
Bibliography
Required Reading
1.
Ellis SE, Leek JT. 2018. How to share data for collaboration, Am Stat. 72(1): 53–57.
2.
Broman, Woo, 2018. Data Organization in Spreadsheets, The American Statistician, 72:1, 2-10
3.
Panesar, A. 2021. Machine Learning and AI for Healthcare.
4.
James, Witten, Hastie, Tibshirani. An Introduction to Statistical Learning. 2023, Chapter 3
5.
James, Witten, Hastie, Tibshirani: An Introduction to Statistical Learning. 2023, Chapter 8
6.
Linear Regression with Knime - Lego Dataset - Knoldus Blogs
7.
Timothy L. Wiemken and Robert R. Kelley. 2020. Machine Learning in Epidemiology and Health Outcomes Research. Annual Review of Public Health 2020 41:1, 21-36,
8.
Sprūdžs, U. 2023. Sirds un asinsrites slimību mirstības riska prognoze nākamajam gadam no anonimizētiem Latvijas veselības aprūpes sistēmas datiem: XGBoost mašīnmācīšanās algoritma iespējamības pārbaude | Akadēmiskā Dzīve (lu.lv)
9.
Cao Xiao, Jimeng Sun, 2021."Introduction to Deep Learning for Healthcare". Springer
Additional Reading
1.
Deep Learning vs. Machine Learning – What’s The Difference?
2.
Activation Functions