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.

Improving Biomedical Information Extraction with Word Embeddings Trained on ClosedDomain Corpora Named Entity Recognition (NER) systems allow complex concepts extraction and text mining from Natural Language documents. Actually, NER systems based on Deep Learning (DL) approaches are able to reach stateoftheart performances when applied to general domain texts. On the other hand, the performances of these systems decrease when they are applied to texts that belong to specific domains, such as the biomedical one. In particular, Biomedical NER (BNER) is a crucial task for the automatic analysis of medical documents, such as Electronic Health Records (EHRs), in order to support the work of physicians and researchers. Thus, new approaches are required to boost BNER systems performances. In this paper we analyze the behaviour of a BNER DL architecture specifically devoted to Italian EHRs, focusing on the contribution of different Word Embeddings (WEs) models used as input text representation layer. The achieved results show the substantial contribution of WEs trained on a closed domain corpus exclusively formed by documents belonging to the biomedical domain. The resulting improvements are comparable with the ones obtained using the most recent and complex neural language models, such as ELMo or BERT, which have a much higher computational complexity if compared with classic WEs approach.