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Basic Courses
This PhD Curriculum offers selectable blocks of 1 Semester-hours, each covering an essential field of the program. The level of these basic lectures is adjusted so as to be understandable by post-graduate students from diverse fields.
The goal of the basic course is to provide a general, open minded insight into different areas of the PhD program. It is the intention to open access, to further acquisition of knowledge, if needed. Lectures will be accompanied by selected textbooks for deepening in the subject whenever possible.
In accordance with the MUW guidelines, the student has to select a minimum of 4 Semester hours, either from the following blocks or – after consent by the coordinator - from basic courses of other accredited MUW-Programs, such as the Biomedical Engineering program. By mutual accreditation of courses, it is aimed at creating a complementing rather than a competing environment.
Exams will be held in written form. Lecturers provide questions for their respective sections and evaluate results according to a score system.
Proposed thematic blocks (1 SWS each) of basic courses. Thematic blocks hold in the same semester are in the same color. These blocks are repeated in a three-semestric cycle.
| Mathematical Basics & Key Algorithms | |||
|---|---|---|---|
| Topic | Detailed contents | Ac. Hrs |
Presented by |
| Linear Algebra | Review | 1 | Karch |
| Differential equations | 1 | Karch | |
| Networks | Matrix Representation & Measures | 1 | Thurner |
| Probability & Stochastics | Basic Concepts | 2 | Hanel |
| Applications: Markov et al. | 1 | von Haeseler | |
| Scaling and fractals | Recent methods of time series analysis | 1 | Thurner |
| Pattern Recognition | Discriminant Analysis | 1 | Dorffner |
| Comlexity Reduction & Ordering | Principal and Independent Components Analysis | 1 | Dorffner |
| Clustering Methods | 1 | Dorffner | |
| Numerical Methods | 1 | Karch | |
| Optimization | Gradient- & Simplex- Algorithms | 1 | Schicho |
| Sequence Alignment | Dynamic Programming and others | 1 | Schreiner |
| Motif Search | Algorithms for Pattern Detection 1 | 1 | von Haeseler |
| Algorithms for Pattern Detection 2 | 1 | von Haeseler | |
| Sum: | 15 | ||
| Advanced Statistical Procedures and Theoretical Concepts | |||
|---|---|---|---|
| Topic | Detailed contents | Ac. Hrs |
Presented by |
| Concepts of Theoretical Statistics | Sufficiency principle, maximum likelihood, point estimation (unbiasedness, consistency), hypothesis testing (Neyman-Pearson Lemma) | 2 | Frommlet |
| Bayesian Statistics | Bayesian thinking, Bayesian computation, likelihood principle | 2 | Heinzl |
| Multiple Testing | Error rates, global tests, single step tests, stepwise procedures, closure principle, simultaneous confidence intervals | 2 | König |
| Nonparametric Methods | One-sample and k-sample rank-based testing procedures, bootstrap and permutation tests | 2 | Graf |
| Regression Models for Observational Studies | Regression models for binary, continuous and time-to-event outcomes, longitudinal models with random effects, development and validation of clinical prediction models, types of biases in observational data analysis, causality, confounding and mediation, models for causal explanation | 7 | Heinze, Dunkler |
| Sum: | 15 | ||
| Information Systems, eHealth & Decision Support | |||
|---|---|---|---|
| Topic | Detailed contents | Ac. Hrs |
Presented by |
| eHealth | Electronic health Records 1 | 1 | Duftschmid |
| Electronic health Records 2 | 1 | Duftschmid | |
| Hospital Information Systems 1 | 1 | Gall | |
| Hospital Information Systems 2 | 1 | Gall | |
| Software Development | Medical software design | 1 | Dorffner |
| FDA compliance & validation | 1 | Dorffner | |
| Clinical Decision Support | Definition, Scope and Challenges | 1 | Adlassnig |
| AI-augmented clinical medicine | Definition Scope and Challenges | 1 | Adlassnig |
| Generation and Formulation of Knowledge | 1 | Adlassnig | |
| Representation of Knowledge: Standardization Efforts | 1 | Samwald | |
| Medical Expert Systems | Definition and Methods | 1 | Adlassnig |
| Differential Diagnostic Medical Expert Systems | 1 | Adlassnig | |
| Knowledge Based Applications | Knowledge-Based Systems in Infection Control and Laboratory Medicine | 1 | Adlassnig |
| Decision Support | Therapy Planning | 1 | |
| Temporal Data Abstraction | 1 | ||
| Sum: | 15 | ||
| Computer Science in Clinical Settings | |||
|---|---|---|---|
| Topic | Detailed contents | Ac. Hrs |
Presented by |
| Telemedicine | Basics and Concepts | 1 | Schicho |
| Application examples | 1 | Schicho | |
| Implementation and Framework | 1 | Schicho | |
| Quality Management | 1 | Schicho | |
| Highfield Magnetic Resonance | Concepts 1 | 1 | Windischberger |
| Concepts 2 | 1 | Windischberger | |
| Applications | 1 | Windischberger | |
| IT 4 Molecular Medicine | Pathway Analysis | 2 | Schreiner |
| Signal Analysis | Diagnostic Systems in Sleep Medicine | 1 | Dorffner |
| Navigation and Robotics | High-Tech Applications in Interventional Radiology | 2 | Kettenbach |
| Augmented Reality and Navigation | Concepts: Registration and Image Fusion | 1 | Schicho |
| Applications 1 | 1 | Schicho | |
| Proteomics regarding Clinical Research | Concepts and Relevance | 1 | Mitulovic |
| Sum: | 15 | ||
| Statistical Methods in Medical Research | |||
|---|---|---|---|
| Topic | Detailed contents | Ac. Hrs |
Presented by | Diagnostic Studies | Concepts, diagnostic accuracy studies | 1 | Heinzl | Clinical Trials Methodology and Meta-Analysis | Study Designs, Phase I-IV Trials, Guidelines, Sources of Bias, Randomization, Blinding, Sample Size Calculation, Systematic Reviews and Meta-Analyses | 4 | Mittlböck | Experimental Design and ANOVA | Basics of the design of experiments, analysis of variance (ANOVA) basics, general linear model formulation for ANOVA, more advanced multi-factorial ANOVA designs | 2 | Dunkler | Sequential and Adaptive Clinical Trials | Principles of group sequential trials, combination tests, sample size reassessment, adaptive trials incorporating treatment selection | 2 | König | Quality of Life and Other Patient-Oriented Outcomes | Patient-reported outcomes, development and validation of instruments, complex scores, classical test theory, item response theory, statistical procedures for analysis of QoL, missingness of QoL, limitations of quantitative instruments | 2 | Stamm | Statistical Issues in Bioinformatics | Analysis of high dimensional datasets, false discovery rate, model selection | 2 | Frommlet | Spatial Epidemiology | Spatial epidemiological studies | 2 | Waldhör |
| Sum: | 15 | ||
| Image & Signal Analysis, Modelling, Simulation & Bioinformatics | |||
|---|---|---|---|
| Topic | Detailed contents | Ac. Hrs |
Presented by |
| Image Analysis | Physical Basics of Medical Image Sources. | 1 | Hanel |
| Image Representation and Operations in Intensity Space. | 1 | Figl | |
| Filtering and Image Transformations | 1 | Hanel | |
| Segmentation | 1 | Hanel | |
| Spatial Transforms and Registration. | 1 | Figl | |
| Rendering and Surface Models | 1 | Figl | |
| Signal Analysis | Spectral & Digital Filtering | 1 | Dorffner |
| Time frequency approach and wavelet analysis | 1 | Dorffner | |
| Modelling & Sim | Molecular Transport Phenomena - Diffusion | 2 | Karch |
| Structural Modelling of Molecules & Mutations | 1 | Stockner | |
| Scoring and Docking | 1 | Stockner | |
| Molecular Dynamics: Basics | 1 | Stockner | |
| Bioinformatics | Molecular Dynamics: Specific Methods for trajectory evaluation | 1 | Stockner |
| Computing for Molecular Medicine | 1 | Schreiner | |
| Reverse engineering of genetic networks | 1 | Hanel | |
| Sum: | 16 | ||
| Complex Systems & Artificial Intelligence | |||
|---|---|---|---|
| Topic | Detailed contents | Ac. Hrs |
Presented by |
| Modern Maths | Game Theory | 1 | Thurner |
| Networks | 1 | Thurner | |
| Information Theory | 1 | Thurner | |
| Complex Systems | Concepts | 1 | Thurner |
| Evolutionary Mechanisms | 1 | Thurner | |
| Applications | 1 | Thurner | |
| Non linear Dynamics | Concepts | 1 | Thurner |
| Applications | 1 | Thurner | |
| Data Analysis | Machine Learning 1: Basic Concepts | 1 | Dorffner |
| Machine Learning 2: Neural Networks & Support Vector Machines | 1 | Dorffner | |
| Vaguenes & Uncertainty | Deep Learning | 2 | Agibetov |
| Semantic Representation | Text Analysis | 1 | Buchberger |
| Biomedical knowlege bases; Conceptualizing, measuring and steering progress in AI | 2 | Samwald | |
| Sum: | 15 | ||
