Integrating population data in a computerized Decision Support System for Head and Neck Cancer

Head and Neck Cancer, the seventh cancer in incidence worldwide, is a heterogeneous disease that encompasses different molecular entities and subgroups with variable risk and potential discriminative treatment options that influence disease outcome. Treatment choice depends mainly on a staging system that has limits on advances cases (Stages III and IV). Because of that, it is needed to find more prognostic factors that can enhance this current classification. Population data contribute on the identification of risk and prognostic factors. Therefore, in the era of precision medicine, the integration of population data with patient clinical data is expected to contribute to a better patient stratification. In this work, carried out in the context of the European Research project 'Big Data to Decide' (BD2Decide), the use of publicly available data sources to improve decision making in Head and Neck Cancer, and their integration in a computerized decision support system have been studied with the contribution of oncologists, epidemiologists and bio-statisticians. The conceptual framework design presented in this paper pretends to support the discovery of prognostic factors, improving risk stratification in Head and Neck Cancer.