Um Estudo sobre a Escolha de Features no Problema de Detecção de Depressão com a Comunidade de Profissionais de Saúde
DOI:
https://doi.org/10.5753/isys.2022.2285Keywords:
Depressão, Redes Sociais, Informática em Saúde MentalAbstract
Compreender os indivíduos, a dinâmica social e o consumo de dados das plataformas de mídia social desperta curiosidade e atenção na comunidade científica e na sociedade. A comunidade científica tem mostrado como a saúde mental de um usuário pode ser afetada pela tecnologia ou por um ambiente digital. Um usuário de uma comunidade online, por exemplo, exposto a constante discurso de ódio pode ter um impacto na sua saúde mental. Já existem esforços nesta área de pesquisa que propõem soluções automatizadas para identificar usuários que necessitam da atenção de um profissional da saúde. No entanto, essas soluções nem sempre utilizam a experiência e o conhecimento da área da saúde na construção de suas contribuições. Para preencher essa lacuna, propomos uma validação qualitativa de features com profissionais da saúde. Essa validação é dividida em duas etapas, e visa soluções de aprendizado de máquina e aprendizado profundo na detecção antecipada de usuários depressivos. Inicialmente, validamos um conjunto de features obtidas em uma revisão da literatura, usando uma entrevista semiestruturada com três psicólogos. Em seguida, aplicamos um questionário online com especialistas do domínio para validar as informações extraídas da primeira etapa. Essa validação nos permitirá ter uma visão detalhada de quão funcionais e práticos são as features comumente usadas em soluções baseadas em aprendizado de máquina e como eles se aproximam da análise clínica.
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