A Data Representation Model for Personalized Medicine

Personalized medicine exploits the patient data, for example, genetic compositions, and key biomarkers. During the data mining process, the key challenges are the information loss, the data types heterogeneity and the time series representation. In this paper, a novel data representation model for personalized medicine is proposed in light of these challenges. The proposed model will account for the structured, temporal and non-temporal data and their types, namely, numeric, nominal, date, and Boolean. After the "Date and Boolean" data transformation, the nominal data are treated by dispersion while several clustering techniques are deployed to control the numeric data distribution. Ultimately, the transformation process results in three homogeneous representations with these representations having only two dimensions to ease the exploration of the represented dataset. Compared to the Symbolic Aggregate Approximation technique, the proposed model preserves the time-series information, conserves as much data as possible and offers multiple simple representations to be explored.