Classificação de conteúdo textual sobre medicamentos presentes em mídia social
Carregando...
Data
2022-10-13
Autores
Viviani, Carolina Moraes [UNIFESP]
Orientadores
Pisa, Ivan Torres [UNIFESP]
Tipo
Dissertação de mestrado
Título da Revista
ISSN da Revista
Título de Volume
Resumo
Objetivo: Classificar conteúdo textual sobre medicamentos presentes em mídia social. Método: Foi realizado um estudo de caso na mídia social Twitter. Um dicionário foi elaborado para identificar as mensagens publicadas na mídia social com conteúdo textual sobre medicamentos. O dicionário foi construído com informações extraídas do anuário estatístico de medicamentos para o ano de 2017. As informações que compõem o dicionário estão relacionadas as substâncias mais comercializadas e mais faturadas para o período. As mensagens identificadas pelo dicionário referem 185 dias do ano de 2017, publicadas em território brasileiro e em língua portuguesa brasileira. Para o processamento do texto técnicas de mineração de dados e texto foram aplicadas. As mensagens identificadas foram avaliadas de forma quantitativa e qualitativa. Para a análise quantitativa foi empregada análises exploratórias, contagens e análise termo frequência. Para a análise qualitativa foi empregada uma análise de conteúdo e uma análise de tópicos. Resultados: O dicionário elaborado possui 1.006 termos, do tipo princípios ativos e marcas comerciais. No total 238.217 mensagens sobre medicamentos foram identificadas com o uso do dicionário. A análise exploratória indicou as categorias de medicamentos mais encontradas para o corpus. As categorias de medicamentos com maior número de representantes no corpus foram: medicamentos que atuam no sistema nervoso (1), substâncias de venda sob prescrição e isentos de prescrição. Em seguida medicamentos que atuam no trato alimentar e metabolismo (2), substâncias de venda sob prescrição e isentos de prescrição. Anti-infecciosos de uso sistêmico (3), substâncias de venda sob prescrição. A análise termo frequência indicou os termos do dicionário mais frequentes nas mensagens para esse corpus Dorflex (1), Dipirona (2), Paracetamol (3), Omeprazol (4) e Codeína (5). No total 6.213 mensagens foram classificadas por um profissional farmacêutico. A análise de conteúdo descreveu aspectos da linguagem, intento dos usuários e relações de uso sobre os medicamentos na mídia social. Uma parte das mensagens foi associada a conceitos da assistência farmacêutica brasileira. Mensagens com menções a reações adversas à medicamentos, suspeita de ineficácia, erros de medicação foram encontradas. A análise de tópicos foi realizada para os medicamentos Dorflex, Dipirona, Paracetamol, Omeprazol, Codeína, Sais de ferro e Vacina de Febre Amarela. Na análise de tópicos aspectos comuns e incomuns entre as mensagens agrupadas sobre um mesmo termo foram descritas. Conclusão: O dicionário permitiu identificar mensagens sobre medicamentos na mídia social. A mídia social estudo de caso foi utilizada pelos consumidores como plataforma para relatar eventos com medicamentos. Algumas mensagens foram associadas a eventos de risco como as reações adversas, suspeita de ineficácia e erros de medicação. Outras mensagens caracterizaram novos aspectos do consumo de medicamentos, como questões comportamentais e afetivas. As mídias sociais podem ser utilizadas como nova fonte de dados em saúde. Há inúmeras possibilidades de aplicações para a assistência farmacêutica. Descritores: Medicamento, Rede social, Dicionário, Serviços de Assistência Farmacêutica
Objective: To classify textual content about drugs present in social media. Method: A case study was carried out on the social media Twitter. A dictionary was created to identify messages published on social media with textual content about drugs. The dictionary was built with information extracted from the statistical yearbook of medicines for the year 2017. The information that makes up the dictionary is related to the most commercialized and most billed substances for the period. The messages identified by the dictionary refer to 185 days in 2017, published in Brazilian territory and in Brazilian Portuguese. For text processing data and text mining techniques were applied. The messages identified were evaluated quantitatively and qualitatively. For the quantitative analysis, exploratory analysis, counting and thermofrequency analysis were used. For the qualitative analysis, a content analysis and a topic analysis were used. Results: The dictionary elaborated has 1,006 terms, such as active principles and trademarks. A total of 238,217 messages about medication were identified using the dictionary. The exploratory analysis indicated the most commonly found medication categories for the corpus. The drug categories with the highest number of representatives in the corpus were: drugs that act on the nervous system (1), prescription and over-the-counter. Then drugs that act on the alimentary tract and metabolism (2), prescription and over-the-counter substances. Systemic anti-infectives (3), prescription drugs. The term frequency analysis indicated the most frequent dictionary terms in the messages for this corpus Dorflex (1), Dipyrone (2), Paracetamol (3), Omeprazole (4) and Codeine (5). A total of 6,213 messages were classified by a pharmaceutical professional. Content analysis described aspects of language, users' intent, and usage relationships about medications on social media. A part of the messages was associated with concepts of Brazilian pharmaceutical care. Messages with mentions of adverse drug reactions, suspected ineffectiveness, medication errors were found. Topical analysis was performed for Dorflex, Dipyrone, Paracetamol, Omeprazole, Codeine, Iron salts and Yellow Fever Vaccine. In the analysis of topics, common and uncommon aspects among the messages grouped under the same term were described. Conclusion: The dictionary made it possible to identify messages about drugs on social media. The social media case study was used by consumers as a platform to report drug events. Some messages were associated with risk eventsix such as adverse reactions, suspected ineffectiveness and medication errors. Other messages featured new aspects of medication consumption, such as behavioral and affective issues. Social media can be used as a new source of health data. There are numerous application possibilities for pharmaceutical care. Keywords: Drugs, Social Media, Dictionary, Pharmaceutical Care
Objective: To classify textual content about drugs present in social media. Method: A case study was carried out on the social media Twitter. A dictionary was created to identify messages published on social media with textual content about drugs. The dictionary was built with information extracted from the statistical yearbook of medicines for the year 2017. The information that makes up the dictionary is related to the most commercialized and most billed substances for the period. The messages identified by the dictionary refer to 185 days in 2017, published in Brazilian territory and in Brazilian Portuguese. For text processing data and text mining techniques were applied. The messages identified were evaluated quantitatively and qualitatively. For the quantitative analysis, exploratory analysis, counting and thermofrequency analysis were used. For the qualitative analysis, a content analysis and a topic analysis were used. Results: The dictionary elaborated has 1,006 terms, such as active principles and trademarks. A total of 238,217 messages about medication were identified using the dictionary. The exploratory analysis indicated the most commonly found medication categories for the corpus. The drug categories with the highest number of representatives in the corpus were: drugs that act on the nervous system (1), prescription and over-the-counter. Then drugs that act on the alimentary tract and metabolism (2), prescription and over-the-counter substances. Systemic anti-infectives (3), prescription drugs. The term frequency analysis indicated the most frequent dictionary terms in the messages for this corpus Dorflex (1), Dipyrone (2), Paracetamol (3), Omeprazole (4) and Codeine (5). A total of 6,213 messages were classified by a pharmaceutical professional. Content analysis described aspects of language, users' intent, and usage relationships about medications on social media. A part of the messages was associated with concepts of Brazilian pharmaceutical care. Messages with mentions of adverse drug reactions, suspected ineffectiveness, medication errors were found. Topical analysis was performed for Dorflex, Dipyrone, Paracetamol, Omeprazole, Codeine, Iron salts and Yellow Fever Vaccine. In the analysis of topics, common and uncommon aspects among the messages grouped under the same term were described. Conclusion: The dictionary made it possible to identify messages about drugs on social media. The social media case study was used by consumers as a platform to report drug events. Some messages were associated with risk eventsix such as adverse reactions, suspected ineffectiveness and medication errors. Other messages featured new aspects of medication consumption, such as behavioral and affective issues. Social media can be used as a new source of health data. There are numerous application possibilities for pharmaceutical care. Keywords: Drugs, Social Media, Dictionary, Pharmaceutical Care