Identification of the relationship between inflammation, infection and depression based on text mining

The aim of this study is to apply an original text analytics algorithm to identify relationships between inflammation associated with viral and bacterial infections and the mechanisms of depression. The developed algorithm includes several stages. At the first stage, it provides a selection of relevant publications. At the second stage, it performs recognition of biological and chemical object names and establishes links between the extracted objects. The results obtained with the algorithm make it possible to identify mechanisms that may be involved in the development of depression, including those associated with infectious diseases that have not been discovered earlier. The study analyses possible relationships between the manifestation of depression and both acute and chronic infections at different ages.