Neurocognitive Approach to Clustering of PubMed Query Results

Abstract

Abstract. Internet literature queries return a long lists of citations, ordered according to their relevance or date. Query results may also be represented using Visual Language that takes as input a small set of semantically related concepts present in the citations. First experiments with such visualization have been done using PubMed neuronal plasticity citations with manually created semantic graphs. Here neurocognitive inspirations are used to create similar semantic graphs in an automated fashion. This way a long list of citations is changed to small semantic graphs that allow semi-automated query refinement and literature based discovery.

Analysis of hormone receptor status in primary and recurrent breast cancer via data mining pathology reports

“…Another promising method for hormone receptor status mining is neurolinguistics. Since the syntax variations in electronic health records are due to differences in expression of natural language, some researchers have achieved a good result in data mining of medical texts, by applying models that imitate the human brain [29, 30]. Given the difficulty in mining hormone receptor status also comes from syntax variability, neurolinguistics modeling may further increase the sensitivity of data detection described in the current study.…”