Mapa de interesses de autores de publicações científicas em Informática em Saúde baseado em Modelagem Autor-Tópico

• Ano de defesa: 2020
• Autor(a) principal: Baptista, Roberto Silva [UNIFESP]
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Paulo (UNIFESP)
• Palavras-chave em Português: Data Mining; Health Informatics; Complex Network Analysis; Topic Model; Mineração De Dados; Informática Em Saúde; Análise De Redes Complexas; Modelagem De Tópicos
• Link de acesso: https://sucupira.capes.gov.br/sucupira/public/consultas/coleta/trabalhoConclusao/viewTrabalhoConclusao.jsf?popup=true&id_trabalho=10927608; https://hdl.handle.net/11600/64770

Introduction: Health Informatics (HI) is an interdisciplinary research field which scientific publication is growing significantly. There are several definitions of HI and research on the thematic structure of HI. Objective: The objective of this research is to present and empirically validate that an application of author-topic modeling and analysis of co-authorship networks in scientific publications makes it possible to highlight interests in research and interaction structures. Methods: Articles from HI journals indexed in PubMed were collected and divided into 5-year periods between 1991 and 2015. An author-topic model was applied to highlight the authors' research interests. Social network analysis techniques were also applied to analyze collaboration between authors. Results: The PubMed retrieval resulted in 69 journals and 76,250 articles. In autor-topic modeling, between 66% and 84% of the topics obtained were labeled. The application of social network analysis techniques showed in all periods that the authors with greater centrality are probably researchers of high relevance in HI. Conclusion: The author-topic models obtained showed robust results, serving as an alternative to evidence the evolution of the HI area from the point of view of the authors' interests identified by the topics obtained. The analysis of coauthorship networks showed the evolution of the global collaboration structure over the years, as well as a local view of the importance of the authors through centrality metrics.