Coh-Metrix-Dementia: automatic analysis of language impairment in dementia using Natural Language Processing

Author(s)

Andre Luiz Verucci da Cunha

• Document type: Master's Dissertation
• Press: São Carlos.
• Institution: Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB)
• Defense date: 2015-10-27

Examining board members

Sandra Maria Aluisio; André Carlos Ponce de Leon Ferreira de Carvalho; Lilian Cristine Hübner; Lucia Specia

• Advisor: Sandra Maria Aluisio

Abstract

(Backgroung) According to the World Health Organization, dementia is a costly social issue, whose management will be a challenge on the coming decades. One common form of dementia is Alzheimers Disease (AD). Another less known syndrome, Mild Cognitive Impairment (MCI), is relevant for being the initial clinically defined stage of AD. Even though MCI is less known by the public, patients with a particular variant of this syndrome, Amestic MCI, evolve to AD in a considerably larger proportion than that of the general population. The diagnosis of dementia and related syndromes is based on the analysis of linguistic and cognitive aspects. Classical exams include fluency, naming, and repetition tests. However, recent research has been recognizing the importance of discourse analysis, specially narrative-based, as a more suitable alternative, specially for MCI detection. (Gap) While qualitative discourse analyses can determine the nature of the patients disease, quantitative analyses can reveal the extent of the existing brain damage. The greatest challenge in quantitative discourse analyses is that a rigorous and thorough evaluation of oral production is very labor-intensive, which hinders its large-scale adoption. In this scenario, computerized analyses become of increasing interest. Automated discourse analysis tools aiming at the diagnosis of language-impairing dementias already exist for the English language, but no such work has been made for Brasilian Portuguese so far. (Goal) This project aims to create a unified environment, entitled Coh-Metrix-Dementia, that will make use of Natural Language Processing and Machine Learning resources and tools to enable automated dementia analysis and classification, initially focusing on AD and MCI. (Hypothesis) Basing our work on Coh-Metrix-Port, the Brazilian Portuguese adaption of Coh-Metrix, and including the adaptation and inclusion of twenty-five new metrics for measuring syntactical complexity, idea density, and text cohesion through latent semantics, it is possible to classify narratives of healthy, AD, and MCI patients, in a machine learning approach, with a precision comparable to classical tests. (Conclusion) In our experiments, it was possible to separate patients in controls, DA, and CCL with 81.7% F-measure, and separate controls and CCL with 90% F-measure. These results indicate that Coh-Metrix-Dementia is a very promising resource in the early detection of language impairment. (AU)

• FAPESP's process: 13/16182-0 - Coh-Metrix-Dementia: Automated Analysis of Language Impairment in Dementia using Natural Language Processing
• Grantee: Andre Luiz Verucci da Cunha
• Support Opportunities: Scholarships in Brazil - Master