Automatic classification of written descriptions by healthy adults: An overview of the application of natural language processing and machine learning techniques to clinical discourse analysis

Abstract

Discourse production is an important aspect in the evaluation of brain-injured individuals. We believe that studies comparing the performance of brain-injured subjects with that of healthy controls must use groups with compatible education. A pioneering application of machine learning methods using Brazilian Portuguese for clinical purposes is described, highlighting education as an important variable in the Brazilian scenario.ObjectiveThe aims were to describe how to:(i) develop machine learning classifiers using features generated by natural language processing tools to distinguish descriptions produced by healthy individuals into classes based on their years of education; and(ii) automatically identify the features that best distinguish the groups.MethodsThe approach proposed here extracts linguistic features automatically from the written descriptions with the aid of two Natural Language Processing tools: Coh-Metrix-Port and AIC. It also includes nine task-specific features (three new ones, two extracted manually, besides description time; type of scene described – simple or complex; presentation order – which type of picture was described first; and age). In this study, the descriptions by 144 of the subjects studied in Toledo18 were used,which included 200 healthy Brazilians of both genders.Results and ConclusionA Support Vector Machine (SVM) with a radial basis function (RBF) kernel is the most recommended approach for the binary classification of our data, classifying three of the four initial classes. CfsSubsetEval (CFS) is a strong candidate to replace manual feature selection methods.

Cognitive Writing Process Characteristics in Alzheimer’s Disease

“… Toledo et al (2014) added an automated component to these analyses. They used a simple (“The Tripping Woman Picture”) and a complex line drawing (“The Traffic Chaos Picture”) that were also used by Forbes-McKay et al (2013 , 2014) .…”

“…Results showed that, for data classification, a specific type of machine learning (i.e., Support Vector Machine with a radial basis function kernel) performed best. Moreover, correlation-based feature selection was the preferred method to replace manual selection with ( Toledo et al, 2014 ). These findings also support the idea that groups can be distinguished on the basis of specific linguistic properties.…”

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

“…Coh-Metrix-Dementia is based on a previous tool for discourse analysis, named Coh-Metrix-Port (SCARTON; ALUíSIO, 2010), which was already used in a clinical discourse analysis study to classify written descriptions of healthy adults (TOLEDO et al, 2014). Based on previous studies using metrics and machine learning classifiers for the English language in clinical settings (e.g.…”

Narrativas produzidas por indivíduos afásicos e indivíduos cognitivamente sadios: análise computadorizada de macro e micro estrutura

“…Foi desenvolvido no NILC e adiciona novas métricas as 48 existentes no Coh-Metrix-Port. As novas métricas do Cohmetrix-Dementia incluem a análise de semântica latente, medidas de diversidade lexical, complexidade sintática e densidade semântica (Toledo et al, 2014;Cunha, 2015). Essa ferramenta mostrou a possibilidade de diferenciação entre indivíduos cognitivamente sadios, com comprometimento cognitivo leve e com doença de Alzheimer, com base na análise computadorizada das amostras de discurso da história da Cinderela.…”

“…Neste sentido, nos últimos anos, tem surgido crescente número de pesquisas relacionadas ao processamento automatizado de dados de discurso. Esses estudos têm demonstrado a possibilidade de realizar diagnóstico diferencial e verificar a evolução de doença por meio de análises utilizando tecnologia computadorizada (MacWhinney, 2000;Fergadiotis et al, 2013;Fraser et al, 2014;Toledo et. al, 2014;Cunha, 2015).…”