Text normalization into medical dictionaries is useful to support clinical task. A typical setting is Pharmacovigilance (PV). The manual detection of suspected adverse drug reactions (ADRs) in narrative reports is time consuming and Natural Language Processing (NLP) provides a concrete help to PV experts. In this paper we carry on experiments for testing performances of MagiCoder, an NLP application designed to extract MedDRA terms from narrative clinical text. Given a narrative description, MagiCoder proposes an automatic encoding. The pharmacologist reviews, (possibly) corrects, and then validates the solution. This drastically reduces the time needed for the validation of reports with respect to a completely manual encoding. In previous work we mainly tested MagiCoder performances on Italian written spontaneous reports. In this paper, we include some new features, change the experiment design, and carry on more tests about MagiCoder. Moreover, we do a change of language, moving to English documents. In particular, we tested MagiCoder on the CADEC dataset, a corpus of manually annotated posts about ADRs collected from social media.

Normalizing Spontaneous Reports into MedDRA: some Experiments with MagiCoder

Combi, Carlo;Zorzi, Margherita;Pozzani, Gabriele;Arzenton, Elena;Moretti, Ugo
2019

Abstract

Text normalization into medical dictionaries is useful to support clinical task. A typical setting is Pharmacovigilance (PV). The manual detection of suspected adverse drug reactions (ADRs) in narrative reports is time consuming and Natural Language Processing (NLP) provides a concrete help to PV experts. In this paper we carry on experiments for testing performances of MagiCoder, an NLP application designed to extract MedDRA terms from narrative clinical text. Given a narrative description, MagiCoder proposes an automatic encoding. The pharmacologist reviews, (possibly) corrects, and then validates the solution. This drastically reduces the time needed for the validation of reports with respect to a completely manual encoding. In previous work we mainly tested MagiCoder performances on Italian written spontaneous reports. In this paper, we include some new features, change the experiment design, and carry on more tests about MagiCoder. Moreover, we do a change of language, moving to English documents. In particular, we tested MagiCoder on the CADEC dataset, a corpus of manually annotated posts about ADRs collected from social media.
Adverse drug reactions
Natural language processing
Healthcare informatics
Pharmacovigilance
Term identification
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/988525
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