In a context of digitalization and modernization of healthcare, automatic analysis of clinical data plays a leading role in improving the quality of care. Since much of the information lies in an unstructured form within clinical notes, it is necessary to make use of modern Natural Language Processing techniques to extract and build structured knowledge from the data. However, clinical texts pose unique challenges due to the extensive usage of i) acronyms, ii) non-standard medical jargons and iii) typos over technical terms. In this paper, we present a prototype spell-checker specifically designed for medical texts written in Italian.

A support for understanding medical notes: Correcting spelling errors in Italian clinical records

Ferrod R.;Brunetti E.;Di Caro L.;Di Francescomarino C.;Marinello R.;Sulis E.
2021-01-01

Abstract

In a context of digitalization and modernization of healthcare, automatic analysis of clinical data plays a leading role in improving the quality of care. Since much of the information lies in an unstructured form within clinical notes, it is necessary to make use of modern Natural Language Processing techniques to extract and build structured knowledge from the data. However, clinical texts pose unique challenges due to the extensive usage of i) acronyms, ii) non-standard medical jargons and iii) typos over technical terms. In this paper, we present a prototype spell-checker specifically designed for medical texts written in Italian.
2021
2021 Workshop on Towards Smarter Health Care: Can Artificial Intelligence Help?, SMARTERCARE 2021
Virtual, Online
2021
CEUR Workshop Proceedings
CEUR-WS
3060
19
28
Clinical notes; Natural language processing; Spelling correction
Ferrod R.; Brunetti E.; Di Caro L.; Di Francescomarino C.; Dragoni M.; Ghidini C.; Marinello R.; Sulis E.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1890153
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