ABSTRACT
Risk modeling in healthcare is both ubiquitous and in its infancy. On the one hand, a significant proportion of medical research focuses on determining the factors that influence the incidence, severity and treatment of diseases, which is a form of risk identification. Those studies typically investigate the micro-level of risk modeling, i.e., the existence of dependences between a reduced set of hypothesized (or demonstrated) risk factors and a focus disease or treatment. On the other hand, the macro-level of risk modeling, i.e., articulating how a large number of such risk factors interact to affect diseases and treatments is not widespread, though essential for medical decision support modeling. By exploiting advances in natural language processing, we believe that information contained in unstructured texts such as medical articles could be extracted to facilitate aggregation into macro-level risk models.
