More data for less disease

Systems Medicine

The aim of systems medicine is to gain knowledge about the development of diseases with the help of patient data, such as genetic data, environmental influences, and lifestyle. One of the reasons the results are improving in this field is that a large amount of data is becoming more available to more patients. Other data from blood, biopsies, and imaging contributes, too.

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Big Data and Artificial Intelligence

Big data in healthcare grabbed headlines in 2009 when Google was able to figure out, nearly in real-time, where swine flu was spreading after its arrival in the USA. This represented a real revolution, as up to then, it took epidemiologists one or two weeks to identify pandemics. Google’s breakthrough was the result of applying a complex model that was based on the correlation between searches by Internet users during a flu outbreak at the company’s site. Source: Cukier und Mayer-Schonberger, „Big Data: A Revolution That Will Transform How We Live, Work, and Think“, John Murray Publishing, October 2013).

The volume of available data has reached the point where it is unusable without the help of artificial intelligence (AI). IBM’s Watson, for example, has access to millions of data sets and has become extremely useful in rare diseases. There are about 3,000 studies published annually, and human practitioners cannot process this amount of information. Watson’s strength only recently became clear when a team of Japanese doctors was unable to diagnose a patient. Watson compared her genetic data with that of 20 million studies. Ten minutes later, a rare blood cancer was diagnosed.

Secure Data Processing with Edge Computing

Digitalized healthcare can speed up and increase accuracy in diagnoses, and help craft specific treatment plans, helping patients in body and soul. An edge data center can offer this highly accurate confidential information with very low latency and very high security.

The sheer volume of medical data is reason enough for edge computing solutions at the edge of the networks. Data is aggregated and filtered close to the source for later storage, visualization, and analysis in computing centers (data mining, analytics). This is a significant relief for the network load. Edge computing also guarantees that research institutions, care providers, and patients receive results quickly, reliably, and securely.

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