MEDICA-tradefair.com spoke with Professor Wolfgang Marquardt about the potential of big data in medicine, and why not all data is the same.
Professor Marquardt, big data has become a type of trendy buzzword. Everyone uses it, but hardly anyone knows what’s behind it. What is big data actually?
Prof. Wolfgang Marquardt: Big data is not just a buzzword without substance. It is not only the size or the amount of data you want to talk about here but also about heterogeneous, unstructured and multifaceted data. The speed of generating data, as well as new possibilities for data processing and interpretation, justify claims about the new potential in science and application. Big data leads to brand-new problem-solving solutions. You can actually illustrate this quite well with the example of medicine.
What potential does big data hold in this area?
The potential is multi-faceted. Let’s take personalized medicine for example: Biological processes that take place in the body of a very particular patient can be detected while using molecular biological omics technologies. In addition, the medical history of the person will increasingly be available in an electronically documented way. This provides personalized data of various types and in a variety of sources. These data has to be brought together on one level to create an overall picture of a person. This lays the foundation to make personalized treatment decisions.
Recently, IBM defined a new business segment with Watson Health. Data from the medical-scientific field are not only made available with the Watson technology, but also linked to a new information source in the context of a specific medical question. The doctor uses the so prepared information for diagnostic or therapeutic decisions. Of course, further research is required to make these types of systems more common and secure.
You could also use big data to describe pathologies with non-invasive methods. Imaging and imaging analysis play an essential role here. If you take this a step further, you can make predictions as to which disease patterns could develop in large groups of patients – so-called cohorts. The predictions could stretch all the way to a survival analysis for severe diseases, pandemics and the like. This data and well-founded knowledge could predict the development of patient groups or individual patients.