Personalized Medicine

All individuals are different, because of differences in their genomes, in the environment they live, in their history and lifestyle. These differences can naturally have a big impact on the way a patient can, for example, react to a pathogen, develop a disease and responds (or not) to a treatment. Personalized Medicine refers to the use of multiple sets of patients’ data, including clinical, molecular and genetic data, to improve diseases’ prevention and diagnosis and to tailor the medical treatments of a patient to the person's individual characteristics.

The main challenge is to find associations between the genotype of a patient and the phenotype (e.g., the onset or course of a disease, the effect of a treatment). As the human genome consists of billions of bases, gigantic amounts of data have to be analysed. Here machine learning comes into play.

Machine learning is one computational method that allows to address the complexity of continuous different sets of data via integration over multiple dimensions. Machine learning algorithms can detect patterns, rules and statistical dependencies in large medical datasets with the ultimate goal to improve healthcare in collaborations with medical researchers. The complexity and ambiguity of sepsis is an ideal challenge for a Personalized Medicine approach.

In Switzerland, a number of national and international programs have committed themselves to the goal of personalized medicine. These include the Swiss Personalized Health Network (SPHN) of the Swiss Academy of Medical Sciences and the strategic focus area Personalized Health and Related Technologies (PHRT) of the ETH Domain. The two programs complement each other and coordinate their activities in order to promote Personalized Health and Personalized Medicine in Switzerland.