Detecting mild cognitive impairment in narratives in Brazilian Portuguese: first steps towards a fully automated system

• Autor(a) principal: Treviso, Marcos Vinícius
• Data de Publicação: 2018
• Outros Autores: dos Santos, Leandro Borges, Shulby, Christopher, Hübner, Lilian Cristine, Mansur, Letícia Lessa, Aluísio, Sandra Maria
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Letras de Hoje (Online)
• Texto Completo: https://revistaseletronicas.pucrs.br/ojs/index.php/fale/article/view/30955
• topic: Clinical diagnosis; Mild cognitive impairment; Automatic sentence segmentation; Syntactic complexity metrics; Automated discourse analysis tools; Diagnóstico clínico; Comprometimento cognitivo leve; Segmentação automática de sentença; Métricas de complexidade sintática; Ferramentas de análise do discurso

In recent years, Mild Cognitive Impairment (MCI) has received a great deal of attention, as it may represent a pre-clinical state of Alzheimer´s disease (AD). In the distinction between healthy elderly (CTL) and MCI patients, automated discourse analysis tools have been applied to narrative transcripts in English and in Brazilian Portuguese. However, the absence of sentence boundary segmentation in transcripts prevents the direct application of methods that rely on these marks for the correct use of tools, such as taggers and parsers. To our knowledge, there are only a few studies evaluating automatic sentence segmentation in transcripts of neuropsychological tests. The purpose of this study is to investigate the impact ofthe automatic sentence segmentation method DeepBond on nine syntactic complexity metrics extracted of transcripts of CTL and MCI patients.