The relationship between skin diseases and mental illnesses has been extensively studied in the literature using cross-sectional epidemiological data. Typically, such data can only measure association (rather than causation) and include only a subset of the diseases we may be interested in. In this work, we complement the evidence from such analyses by learning a dynamic Bayesian network of 13 conditions from the Google search trends dataset. The resulting network model can represent both cyclic and acyclic causal relationships, is easy to interpret and accounts for the spatio-temporal trends in the data in a probabilistically rigorous way.