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Improving Drug–Drug Interaction Extraction with Gaussian Noise

Drug–Drug Interactions (DDIs) produce essential and valuable insights for healthcare professionals, since they provide data on the impact of concurrent administration of medications to patients during therapy. In that sense, some relevant works, related to the DDIExtraction2013 Challenge, are availa...

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Detalles Bibliográficos
Autores principales: Molina, Marco, Jiménez, Cristina, Montenegro, Carlos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385013/
https://www.ncbi.nlm.nih.gov/pubmed/37514010
http://dx.doi.org/10.3390/pharmaceutics15071823
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author Molina, Marco
Jiménez, Cristina
Montenegro, Carlos
author_facet Molina, Marco
Jiménez, Cristina
Montenegro, Carlos
author_sort Molina, Marco
collection PubMed
description Drug–Drug Interactions (DDIs) produce essential and valuable insights for healthcare professionals, since they provide data on the impact of concurrent administration of medications to patients during therapy. In that sense, some relevant works, related to the DDIExtraction2013 Challenge, are available in the current technical literature. This study aims to improve previous results, using two models, where a Gaussian noise layer is added to achieve better DDI relationship extraction. (1) A Piecewise Convolutional Neural Network (PW-CNN) model is used to capture relationships among pharmacological entities described in biomedical databases. Additionally, the model incorporates multichannel words to enrich a person’s vocabulary and reduce unfamiliar words. (2) The model uses the pre-trained BERT language model to classify relationships, while also integrating data from the target entities. After identifying the target entities, the model transfers the relevant information through the pre-trained architecture and integrates the encoded data for both entities. The results of the experiment show an improved performance, with respect to previous models.
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spelling pubmed-103850132023-07-30 Improving Drug–Drug Interaction Extraction with Gaussian Noise Molina, Marco Jiménez, Cristina Montenegro, Carlos Pharmaceutics Article Drug–Drug Interactions (DDIs) produce essential and valuable insights for healthcare professionals, since they provide data on the impact of concurrent administration of medications to patients during therapy. In that sense, some relevant works, related to the DDIExtraction2013 Challenge, are available in the current technical literature. This study aims to improve previous results, using two models, where a Gaussian noise layer is added to achieve better DDI relationship extraction. (1) A Piecewise Convolutional Neural Network (PW-CNN) model is used to capture relationships among pharmacological entities described in biomedical databases. Additionally, the model incorporates multichannel words to enrich a person’s vocabulary and reduce unfamiliar words. (2) The model uses the pre-trained BERT language model to classify relationships, while also integrating data from the target entities. After identifying the target entities, the model transfers the relevant information through the pre-trained architecture and integrates the encoded data for both entities. The results of the experiment show an improved performance, with respect to previous models. MDPI 2023-06-26 /pmc/articles/PMC10385013/ /pubmed/37514010 http://dx.doi.org/10.3390/pharmaceutics15071823 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Molina, Marco
Jiménez, Cristina
Montenegro, Carlos
Improving Drug–Drug Interaction Extraction with Gaussian Noise
title Improving Drug–Drug Interaction Extraction with Gaussian Noise
title_full Improving Drug–Drug Interaction Extraction with Gaussian Noise
title_fullStr Improving Drug–Drug Interaction Extraction with Gaussian Noise
title_full_unstemmed Improving Drug–Drug Interaction Extraction with Gaussian Noise
title_short Improving Drug–Drug Interaction Extraction with Gaussian Noise
title_sort improving drug–drug interaction extraction with gaussian noise
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385013/
https://www.ncbi.nlm.nih.gov/pubmed/37514010
http://dx.doi.org/10.3390/pharmaceutics15071823
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