Extracting Adverse Drug Effects from User Experiences: A Baseline
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.6/9114 |
Resumo: | It has been proved that pharmacovigilance benefits from the analysis and extraction of user-generated data from blogs, medical forums or other social networks, regarding adverse effect mentions or complaints that occur from taking certain drugs. Data mining, machine learning, pattern recognition, content summarization, and natural language processing techniques are often used in this field with promising results. However, there are still several difficulties concerning the extraction, as the highly domain-specific vocabulary presents a few challenges. This is mainly because patients like to use idiomatic or vernacular expressions along with descriptive symptom explanations, which tend to deviate from grammatical rules or expected terms. To address this issue, we propose a well-curated baseline. We believe that building a specific lexicon, identifying common linguistic patterns and observing certain phrasal structures is key to first understanding how a user generates contents online. From there, we can later develop sets of tailored rules that will allow data classification/extraction systems to potentially improve their efficiency at these tasks. |
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Extracting Adverse Drug Effects from User Experiences: A BaselinePharmacovigilanceAdverse effectsInformation extractionNatural language processingIt has been proved that pharmacovigilance benefits from the analysis and extraction of user-generated data from blogs, medical forums or other social networks, regarding adverse effect mentions or complaints that occur from taking certain drugs. Data mining, machine learning, pattern recognition, content summarization, and natural language processing techniques are often used in this field with promising results. However, there are still several difficulties concerning the extraction, as the highly domain-specific vocabulary presents a few challenges. This is mainly because patients like to use idiomatic or vernacular expressions along with descriptive symptom explanations, which tend to deviate from grammatical rules or expected terms. To address this issue, we propose a well-curated baseline. We believe that building a specific lexicon, identifying common linguistic patterns and observing certain phrasal structures is key to first understanding how a user generates contents online. From there, we can later develop sets of tailored rules that will allow data classification/extraction systems to potentially improve their efficiency at these tasks.This work was supported by Project NORTE-01- 0145-FEDER-000016 (NanoSTIMA), which is financed by the North Portugal Regional Operational Programme (NORTE2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF)Institute of Electrical and Electronics EngineersuBibliorumAbrantes, DiogoCordeiro, João2020-02-07T14:03:13Z2018-06-182018-06-18T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.6/9114engD. Abrantes and J. Cordeiro, "Extracting Adverse Drug Effects from User Experiences: A Baseline," 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS), Karlstad, 2018, pp. 405-410.10.1109/CBMS.2018.00077metadata only accessinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-14T04:24:31Zoai:ubibliorum.ubi.pt:10400.6/9114Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:49:20.332654Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Extracting Adverse Drug Effects from User Experiences: A Baseline |
title |
Extracting Adverse Drug Effects from User Experiences: A Baseline |
spellingShingle |
Extracting Adverse Drug Effects from User Experiences: A Baseline Abrantes, Diogo Pharmacovigilance Adverse effects Information extraction Natural language processing |
title_short |
Extracting Adverse Drug Effects from User Experiences: A Baseline |
title_full |
Extracting Adverse Drug Effects from User Experiences: A Baseline |
title_fullStr |
Extracting Adverse Drug Effects from User Experiences: A Baseline |
title_full_unstemmed |
Extracting Adverse Drug Effects from User Experiences: A Baseline |
title_sort |
Extracting Adverse Drug Effects from User Experiences: A Baseline |
author |
Abrantes, Diogo |
author_facet |
Abrantes, Diogo Cordeiro, João |
author_role |
author |
author2 |
Cordeiro, João |
author2_role |
author |
dc.contributor.none.fl_str_mv |
uBibliorum |
dc.contributor.author.fl_str_mv |
Abrantes, Diogo Cordeiro, João |
dc.subject.por.fl_str_mv |
Pharmacovigilance Adverse effects Information extraction Natural language processing |
topic |
Pharmacovigilance Adverse effects Information extraction Natural language processing |
description |
It has been proved that pharmacovigilance benefits from the analysis and extraction of user-generated data from blogs, medical forums or other social networks, regarding adverse effect mentions or complaints that occur from taking certain drugs. Data mining, machine learning, pattern recognition, content summarization, and natural language processing techniques are often used in this field with promising results. However, there are still several difficulties concerning the extraction, as the highly domain-specific vocabulary presents a few challenges. This is mainly because patients like to use idiomatic or vernacular expressions along with descriptive symptom explanations, which tend to deviate from grammatical rules or expected terms. To address this issue, we propose a well-curated baseline. We believe that building a specific lexicon, identifying common linguistic patterns and observing certain phrasal structures is key to first understanding how a user generates contents online. From there, we can later develop sets of tailored rules that will allow data classification/extraction systems to potentially improve their efficiency at these tasks. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-06-18 2018-06-18T00:00:00Z 2020-02-07T14:03:13Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.6/9114 |
url |
http://hdl.handle.net/10400.6/9114 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
D. Abrantes and J. Cordeiro, "Extracting Adverse Drug Effects from User Experiences: A Baseline," 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS), Karlstad, 2018, pp. 405-410. 10.1109/CBMS.2018.00077 |
dc.rights.driver.fl_str_mv |
metadata only access info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
metadata only access |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Institute of Electrical and Electronics Engineers |
publisher.none.fl_str_mv |
Institute of Electrical and Electronics Engineers |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799136385849884672 |