Written Test

This lecture comprises different areas of Medical Data Science. Data Science or statistical machine learning methods have the potential to transform personal health care over the coming years. Advances in the technologies have generated large biological data sets. In order to gain insights that can then be used to improve preventive care or treatment of patients, these big data have to be stored in a way that enables fast querying of relevant characteristics of the data and consequently building statistical models that represent the dependencies between variables. These models can then be utilized to derive new biomedical principals, provide evidence for or against certain hypotheses, and to assist medical professionals in their decision process.

Specific topics are:
• Gaining new insights from medical data
• Modeling uncertainty in medical data science models
• Making medical findings available through interpretable decision support systems

Method-wise, the lecture introduces methods for GWAS analyses (e.g., LMMs), methods for sequence analysis (e.g., kernel methods), methods for “small n problems” (e.g., domain adaptation, transfer learning, and multitask learning),
methods for data integration (advanced unsupervised learning methods), methods for learning probabilistic Machine Learning models (e.g., graphical models), methods for large data sets (e.g., deep learning models).

The students are capable of explaining the most important terms, methods and theories in the data science area with focus on the analysis of biomedical data. They are enabled to decide which type of methods fit to which kind of data sets. The students can critically reflect on shortcomings of state-of-the-art methods
to potentially come up with ideas for extending or improving the methods.

Trevor Hastie, Robert Tibshirani, Jerome Friedman: The Elements of Statistical
Learning, Springer Series in Statistics.
Further books will be announced in the first lecture. / recommended: Machine learning: theory and algorithms or Introduction to Statistical
Machine Learning for Bioinfos and Medicine Infos