  The relationship between skin diseases and mental illnesses has been
extensively studied 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 paper, we complement
the evidence from such analyses by learning an overarching causal network model
over twelve health conditions from the Google Search Trends Symptoms public
data set.
  We learned the causal network model using a dynamic Bayesian network, which
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. The causal network confirms a large number of
cyclic relationships between the selected health conditions and the interplay
between skin and mental diseases. For acne, we observe a cyclic relationship
with anxiety and attention deficit hyperactivity disorder (ADHD) and an
indirect relationship with depression through sleep disorders. For dermatitis,
we observe directed links to anxiety, depression and sleep disorders and a
cyclic relationship with ADHD. We also observe a link between dermatitis and
ADHD and a cyclic relationship between acne and ADHD. Furthermore, the network
includes several direct connections between sleep disorders and other health
conditions, highlighting the impact of the former on the overall health and
well-being of the patient.
  Mapping disease interplay, indirect relationships, and the key role of
mediators, such as sleep disorders, will allow healthcare professionals to
address disease management holistically and more effectively. Even if we
consider all skin and mental diseases jointly, each disease subnetwork is
unique, allowing for more targeted interventions.
