Denmark is one of the most well-established societies in the world. We have registers and databases that collect a wealth of data that can be used to uncover contexts and point to possible causes and aftereffects, to and of e.g. Diseases. Often, you run and search such data based on a particular hypothesis, which can then either be statistically rebuttable or confirmed. You can also do the reverse – start by finding statistically significant correlations, and then formulate a hypothesis that can then be verified in an independent data material.
The lecture will, among other things, describe this kind of non-hypothesis-driven analysis of the entire Danish country's patient register, where more than 6 million different patients 45 million admissions have been used as a basis. For example, you can Clarify which diseases are much more frequent than one should expect to occur simultaneously, and also in what order diseases are detected and in this way make a kind of cartoon over the course of the disease. Disease correlations can be used to create disease networks that describe how interconnected and how close thousands of different diseases lie to each other in the Danish population. Data in the country patient register can also be combined with electronic patient terminals, so that the disease networks are based on the very fine-grain disease profile of each patient. This will result in different patient groups within each disease area with symtome images that are much more detailed than the data registers often contain. Correlations between two diseases can, for example, be Because a particular defect gene influences both of them. Most complex diseases are related to the hundreds of genes, and one of the major perspectives is to integrate the disease correlations with the networks of human 25.000 genes themselves forming, and which creates cells, organs and whole organisms through their mutually Interaction.