Neurolinguistic approach to vector representation of medical concepts

Putative brain processes responsible for understanding language are based on spreading activation in semantic networks, providing enhanced representations that involve concepts not found directly in the text. Approximation of this process is of great practical and theoretical interest. Vector model should reflect activations of various concepts in the brain spreading through associative network. Medical ontologies are used to select concepts of specific semantic type and add to each of them related concepts, providing expanded vector representations. The process is constrained by selection of useful extensions for the classification task. Short hospital discharge summaries are used to illustrate how this process works on a real, very noisy data. Results show significantly improved clustering and classification accuracy. A practical approach to mapping of associative networks of the brain involved in processing of specific concepts is presented.