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Towards an Italian Healthcare Knowledge Graph

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Similarity Search and Applications (SISAP 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13058))

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Abstract

Electronic Health Records (EHRs), Big Data, Knowledge Graphs (KGs) and machine learning can potentially be a great step towards the technological shift from the one-size-fit-all medicine, where treatments are based on an equal protocol for all the patients, to the precision medicine, which takes count of all their individual information: lifestyle, preferences, health history, genomics, and so on. However, the lack of data which characterizes low-resource languages is a huge limitation for the application of the above-mentioned technologies. In this work, we will try to fill this gap by means of transformer language models and few-shot approaches and we will apply similarity-based deep learning techniques on the constructed KG for downstream applications. The proposed architecture is general and thus applicable to any low-resource language.

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Notes

  1. 1.

    https://www.medicitalia.it/.

  2. 2.

    https://github.com/idb-ita/GilBERTo.

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Correspondence to Marco Postiglione .

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Postiglione, M. (2021). Towards an Italian Healthcare Knowledge Graph. In: Reyes, N., et al. Similarity Search and Applications. SISAP 2021. Lecture Notes in Computer Science(), vol 13058. Springer, Cham. https://doi.org/10.1007/978-3-030-89657-7_29

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  • DOI: https://doi.org/10.1007/978-3-030-89657-7_29

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