Impact of social media data on postmarket safety evaluation of medicines: a literature review on automatic mining initiatives of adverse drug reactions
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Data de Publicação: | 2021 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/125980 |
Resumo: | Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence |
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Impact of social media data on postmarket safety evaluation of medicines: a literature review on automatic mining initiatives of adverse drug reactionsSocial MediaReal World DataPharmacovigilanceMedicinesMachine LearningAdverse drug reactionsDissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceIntroduction/Background: The impact of adverse drug reactions (ADRs) on public health and national healthcare systems is substantial. The current pharmacovigilance method is time-consuming, incomplete and prone to data loss. Also, due to characteristics inherent to the reporting process, a great portion of ADRs are never reported. Social media (SM) data, due to its volume and immediacy, shows promise for a patient centered way of reporting, and has received increasing attention over the last few years. Objectives/Methodology: In this research project the author proposes to evaluate how can ADR automatic detection from social media contribute for pharmacovigilance, through a systematic literature review. The review included articles published over the last five years, accounting to 33 publications that were retrieved and reviewed in detail. Discussion: Several aspects have proven to be critical when developing SM based ADR mining - the main purpose of the analysis (detection of posts containing ADRs and the extraction of specific ADRdrug pairs), the approach (lexicon or machine learning based), and the type of platform used (healthfocused or general use). The studies have shown a prevalence of machine learning (ML) based approaches, from which supervised learning is the most popular method, despite the rising trend against the need for costly and time-consuming annotation of data. Mixed approaches have often been used as they seem to derive better performance, whether in combining data sources from general platforms and disease forums, or using distinct sources of annotated data sets, such as biomedical corpus to increase algorithms strength, and even the combination of ML approaches with lexicon based features. Conclusions/Limitations: The end goal of ADR mining from social media is to be able to identify drugs that are either frequently related to ADRs, or those that are associated with previously unknown ADRs. Combining data from multiple sources will contribute to prevent the impact of serious or previously unknown ADRs, focusing on the issues most pertinent to patients, and will provide a broader safety profile of any medication, with benefits for patients, health systems, companies and regulatory agencies. SM data comes with its specificities (informal language, semantic confusion and ambiguity) that lead to analysis hurdles; hence the method and approach used must be adapted to the purpose of investigation and resources available. Norms and agreed practices to guide these efforts are needed, considering ethical issues, data quality and governance. The progress in information technology and the need to consider patient experience should motivate future research on social media surveillance for complementing conventional pharmacovigilance with patient centric and real time ways of reporting.Victorino, Guilherme Hidalgo Barata MartinsRUNMendes, Ana Catarina Simões Martins2021-10-12T11:02:38Z2021-09-072021-09-07T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/125980TID:202775321enginfo: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:RCAAP2024-03-11T05:06:42Zoai:run.unl.pt:10362/125980Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:45:49.808340Repositório 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 |
Impact of social media data on postmarket safety evaluation of medicines: a literature review on automatic mining initiatives of adverse drug reactions |
title |
Impact of social media data on postmarket safety evaluation of medicines: a literature review on automatic mining initiatives of adverse drug reactions |
spellingShingle |
Impact of social media data on postmarket safety evaluation of medicines: a literature review on automatic mining initiatives of adverse drug reactions Mendes, Ana Catarina Simões Martins Social Media Real World Data Pharmacovigilance Medicines Machine Learning Adverse drug reactions |
title_short |
Impact of social media data on postmarket safety evaluation of medicines: a literature review on automatic mining initiatives of adverse drug reactions |
title_full |
Impact of social media data on postmarket safety evaluation of medicines: a literature review on automatic mining initiatives of adverse drug reactions |
title_fullStr |
Impact of social media data on postmarket safety evaluation of medicines: a literature review on automatic mining initiatives of adverse drug reactions |
title_full_unstemmed |
Impact of social media data on postmarket safety evaluation of medicines: a literature review on automatic mining initiatives of adverse drug reactions |
title_sort |
Impact of social media data on postmarket safety evaluation of medicines: a literature review on automatic mining initiatives of adverse drug reactions |
author |
Mendes, Ana Catarina Simões Martins |
author_facet |
Mendes, Ana Catarina Simões Martins |
author_role |
author |
dc.contributor.none.fl_str_mv |
Victorino, Guilherme Hidalgo Barata Martins RUN |
dc.contributor.author.fl_str_mv |
Mendes, Ana Catarina Simões Martins |
dc.subject.por.fl_str_mv |
Social Media Real World Data Pharmacovigilance Medicines Machine Learning Adverse drug reactions |
topic |
Social Media Real World Data Pharmacovigilance Medicines Machine Learning Adverse drug reactions |
description |
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-10-12T11:02:38Z 2021-09-07 2021-09-07T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/125980 TID:202775321 |
url |
http://hdl.handle.net/10362/125980 |
identifier_str_mv |
TID:202775321 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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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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