A Big Data Approach for Health Data Information Retrieval

In this paper we address the problem of analyzing biomedical data collection with the purpose of searching for semantic similarity among textual documents. In details, we leverage Word Embeddings models obtained by word2vec algorithm and a specific Big Data architecture for their management, defining an approach able to permit the retrieving of semantic similar texts among a huge biomedical text corpus. The proposed architecture has been developed with the purpose of improving a previous implementation, lowering the computational time and allowing in this way the use of the whole PubMed library as dataset, proving also the usability of this methodology in a real context.