A New Framework for Distilling Higher Quality Information from Health Data via Social Network Analysis

Abstract

Personalized medicine as well as systems biology poses the challenge of developing new models to connect health data coming from many different flows and extract from them new information to support clinicians in their therapeutic activity. In this scenario we developed a novel framework, tailored to clinicians needs, which exploits the strength of the social network model to provide a representation of the health care system as a whole. In this paper we also propose a data analysis approach inspired to the humans' cognitive process where the awareness of a phenomenon is the result of an exploration step in which situations of possible interest are identified, and a subsequent in-depth examination step in which the phenomenon is characterized. Experiments have shown that our framework is able to provide effective answers to complex enquiries submitted by clinicians for which standard statistical methods fail.