Imprecise Data and Their Impact on Translational Research in Medicine

Abstract

The medical field expects from big data essentially two main results: the ability to build predictive models and the possibility of applying them to obtain accurate patient risk profiles and/or health trajectories. Note that the paradigm of precision has determined that similar challenges need to be faced in both population and individualized studies, namely the need of assembling, integrating, modeling, and interpreting data from a variety of information sources and scales potentially influencing disease from onset to progression. In many cases, data require computational treatment through solutions for otherwise intractable problems. However, as precision medicine remains subject to a substantial amount of data imprecision and lack of translational impact, a revision of methodological inference approaches is needed. Both the relevance and the usefulness of such revision crucially deal with the assimilation of data features dynamically interconnected.