In this paper we present a Natural Language Processing (NLP) pipeline to automatically extract medical concepts from a free text written in a language other than English. To do so, we use common NLP techniques and the metathesaurus of Unified Medical Language System (UMLS). Specifically, our goal is to automatically extract ontological concepts representing which part of the human body is injured and what is the nature of the injury, given an Italian textual description of a work accident. We start by partitioning the text into tokens and assigning to each token its part-of-speech, and then use an appropriate tool to extract relevant concepts to be searched within UMLS. We tested our system on a public large repository containing textual descriptions of work accidents produced by INAIL. Experimental results confirm that our system is able to correctly extract relevant medical concepts from texts written in Italian.

Extraction of medical concepts from Italian natural language descriptions

Azzalini F.;Piantella D.;Rabosio E.;Tanca L.
2021

Abstract

In this paper we present a Natural Language Processing (NLP) pipeline to automatically extract medical concepts from a free text written in a language other than English. To do so, we use common NLP techniques and the metathesaurus of Unified Medical Language System (UMLS). Specifically, our goal is to automatically extract ontological concepts representing which part of the human body is injured and what is the nature of the injury, given an Italian textual description of a work accident. We start by partitioning the text into tokens and assigning to each token its part-of-speech, and then use an appropriate tool to extract relevant concepts to be searched within UMLS. We tested our system on a public large repository containing textual descriptions of work accidents produced by INAIL. Experimental results confirm that our system is able to correctly extract relevant medical concepts from texts written in Italian.
CEUR Workshop Proceedings
EHR
NLP
Ontology
Work accident
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1191389
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