Study obligations
Passing two courses. The course B90290 Introduction to Biomedical Informatics is compulsory. Students can choose the second course from the courses listed below or from courses offered by other Field Boards in the Biomedicine program.

Elective courses offered by the Field Board of Biomedical Informatics:

B90182 Modeling of biomedical systems
B90072 Physiology regulation system in normal and pathology
B90278 Process modeling in health information systems
B90287 Data science v R
B90288 eHealth and eGovernment in the Czech Republic
B90289 Artificial Intelligence and its use in medicine
B90291 Python programming basics
B90292 Methodology of science and bioinformatics
It is also possible to take courses organized by ÚTIA AV ČR Variational Methods in Image Processing and Digital Image Processing (ÚTIA AV ČR - Istitute of Information Theory and Automation Czech Academy of Sciences).

Part of the study plan is an English language exam (the language exam at the faculty’s institute of languages, the State Language Examination or an internationally recognized type of examination (i.e. TOEFL, Cambridge Certificate).

At least two original publications on the topic of the thesis in journals with IF and passing the state doctoral examination are prerequisites to the defence of the dissertation.

For the defense, the student submits an abstract of the thesis (a brief summary of the entire dissertation, including a summary in English and a list of references) and two copies of the dissertation.

Requirements on creative activities
Publication of at least two original articles (on a topic related to the general topic of study) in journals with IF (one as first author), where IF > 0.5.

Requirements on placement taken
A stay at a foreign workplace lasting at least one month. This may be replaced (as assessed by the chair of the Field Board) by participation in an international project or another form of direct international cooperation or participation in an international congress.

State doctoral examination
Requisites for commencement of the state exam:
Passing two above listed courses.
One publication on the topic of the thesis in a journal with IF.
English language exam.

State doctoral examination procedure:
The student submits research work concerning the topic of the thesis and outlines the proposition of the thesis.
He/She defends it in the qualified discussion.
The student answers two theoretical questions. Students can choose either two questions from informatics or one from statistics and one from informatics.

Questions on the state doctoral examination in biomedical informatics
Informatics set

1. Concept of data, information, knowledge, uncertainty and entropy
2. Decision making in medicine, specificity, sensitivity and predictive value
3. Expert systems and artificial intelligence in medicine
4. Use of biomedical information sources
5. Internet in medicine, health information quality assessment
6. Neural networks, Bayesian networks and types of neural networks
7. Decision theory in medicine, decision support systems
8. Cybernetic security, data protection in medicine, electronic signature
9. Hospital information system, medical record, medication record
10. Structure and principles of information systems in healthcare
11. Electronic data networks their hierarchy in healthcare.
12. International classification of diseases
13. Data mining methods
14. Mathematical modeling
15. Evidence-based medicine, translational medicine
16. Clinical studies, principles and classification
17. Therapeutic algorithms and their formalization
18. Biological signals, basic concepts, classification and analysis
19. Image analysis and processing
20. Telemedicine
21. Biomedical informatics outlook
22. Health insurance, economical models of health care
23. National Health Information System

Medical statistics
1. Descriptive characteristics of continuous and categorical random variables, graphical representation of data
2. Population and random sample, location and scale parameter of continuous random variables a its sample estimates, moments of continuous random variables
3. Continuous and discrete probability distributions, normal (Gaussian) and uniform distribution, alternative and binomial distribution
4. Statistical testing – random sample, representative sample, medical hypothesis, null and alternative statistical hypothesis, test statistic, significance level of statistical test, critical value, observed significance level (p-value), statistical software
5. Hypotheses testing and confidence intervals
6. Testing hypothesis about the mean of continuous random variable – parametric one-sample and two-sample tests, paired tests, nonparametric tests
7. Categorial data analysis – Chi-squared test, Fischer test
8. Correlation analysis – correlation and covariance matrix, types of correlation (Pearson, Kendall, Spearman), correlation and causality, uncorrelation vs. independence
9. Time series, time trend, periodicity
10. Multivariate methods – discriminant, factor and cluster analysis, principal components, graphical methods
11. Health statistics and clinical registries
12. Phases of clinical trials I - IV
13. Survival analysis (Kaplan-Meier estimate, Cox PH model and its variants for the case of violated PH assumptions)
14. Linear regression and problem of collinearity of the predictors
15. Analysis of variance
16. Generalised linear regression (logistic regression, Poisson regression)
17. Akaike (AIC) a Bayesian information criterium (BIC), optimal model selection
18. Parametric and nonparametric statistical tests of hypotheses (a general comparison)
19. Multiple statistical tests and inflation of statistical significance level alpha, simultaneous statistical tests
20. Euclidean and Mahalanobis statistical distance
21. Classification methods, regression and classification trees
22. Exploratory and confirmative analysis, meta-analysis
23. Bayes theorem, Bayesian vs. frequentist (classical) statistics