Impact of social media data on postmarket safety evaluation of medicines: a literature review on automatic mining initiatives of adverse drug reactions

Detalhes bibliográficos
Autor(a) principal: Mendes, Ana Catarina Simões Martins
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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spelling 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
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
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
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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