Inferring Skin-Brain-Skin Connections from Infodemiology Data using Dynamic Bayesian Networks

Abstract

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 a dynamic Bayesian network over 12 health conditions from a Google search trends public data set. 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. The average R^2 for a condition given the values of all conditions in the previous week is 0.67: in particular, 0.42 for acne, 0.85 for asthma, 0.58 for ADHD, 0.87 for burn, 0.76 for erectile dysfunction, 0.88 for scars, 0.57 for alcohol disorders, 0.57 for anxiety, 0.53 for depression, 0.74 for dermatitis, 0.60 for sleep disorders and 0.66 for obesity. Results confirm the large number of cyclic relationships between the selected health conditions and the interplay between skin and mental diseases. For acne, we observed a cyclic relationship with anxiety and attention deficit hyperactivity disorder (ADHD) and an indirect relationship with depression through sleep disorders. For dermatitis, we observed directed links to anxiety, depression and sleep disorder 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 disorder, will allow healthcare professionals to address disease management holistically and more effectively. Even if all skin and mental diseases are considered jointly, each disease subnetwork is unique, allowing for more targeted interventions.