Personalized or precision medicine is a new paradigm that holds great promise for\nindividualized patient diagnosis, treatment, and care. However, personalized medicine has only\nbeen described on an informal level rather than through rigorous practical guidelines and statistical\nprotocols that would allow its robust practical realization for implementation in day-to-day clinical\npractice. In this paper, we discuss three key factors, which we consider dimensions that effect\nthe experimental design for personalized medicine: (I) phenotype categories; (II) population size;\nand (III) statistical analysis. This formalization allows us to define personalized medicine from a\nmachine learning perspective, as an automized, comprehensive knowledge base with an ontology\nthat performs pattern recognition of patient profiles.
Loading....