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Word-Embeddings and Grammar Features to Detect Language Disorders in Alzheimer’s Disease Patients

Detección de desórdenes de lenguaje de pacientes con enfermedad de Alzheimer usando embebimientos de palabras y características gramaticales

dc.creatorGuerrero-Cristancho, Juan S.
dc.creatorVásquez-Correa, Juan C.
dc.creatorOrozco-Arroyave , Juan R.
dc.date2020-01-30
dc.identifierhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1387
dc.identifier10.22430/22565337.1387
dc.descriptionAlzheimer's Disease (AD) is a progressive neurodegenerative disorder that affects the language production and thinking capabilities of patients. The integrity of the brain is destroyed over time by interruptions in the interactions between neuron cells and associated cells required for normal brain functioning. AD comprises deterioration of the communicative skills, which is reflected in deficient speech that usually contains no coherent information, low density of ideas, and poor grammar. Additionally, patients exhibit difficulties to find appropriate words to structure sentences. Multiple ongoing studies aim to detect the disease considering the deterioration of language production in AD patients. Natural Language Processing techniques are employed to detect patterns that can be used to recognize the language impairments of patients. This paper covers advances in pattern recognition with the use of word-embedding and word-frequency features and a new approach with grammar features. We processed transcripts of 98 AD patients and 98 healthy controls in the Pitt Corpus of the Dementia-Bank database. A total of 1200 word-embedding features, 1408 Term Frequency—Inverse Document Frequency features, and 8 grammar features were extracted from the selected transcripts. Three models are proposed based on the separate extraction of such feature sets, and a fourth model is based on an early fusion strategy of the proposed feature sets. All the models were optimized following a Leave-One-Out cross validation strategy. Accuracies of up to 81.7 % were achieved using the early fusion of the three feature sets. Furthermore, we found that, with a small set of grammar features, accuracy values of up to 72.8 % were obtained. The results show that such features are suitable to effectively classify AD patients and healthy controls.en-US
dc.descriptionLa enfermedad de Alzheimer es un desorden neurodegenerativo-progresivo que afecta la producción de lenguaje y las capacidades de pensamiento de los pacientes. La integridad del cerebro es destruida con el paso del tiempo por interrupciones en las interacciones entre neuronas y células, requeridas para su funcionamiento normal. La enfermedad incluye el deterioro de habilidades comunicativas por un habla deficiente, que usualmente contiene información inservible, baja densidad de ideas y habilidades gramaticales. Adicionalmente, los pacientes presentan dificultades para encontrar palabras apropiadas y así estructurar oraciones. Por lo anterior, hay investigaciones en curso que buscan detectar la enfermedad considerando el deterioro de la producción de lenguaje. Así mismo, se están usando técnicas de procesamiento de lenguaje natural para detectar patrones y reconocer las discapacidades del lenguaje de los pacientes. Por su parte, este artículo se enfoca en el uso de características basadas en embebimiento y frecuencia de palabras, además de hacer una nueva aproximación con características gramaticales para clasificar la enfermedad de Alzheimer. Para ello, se consideraron transcripciones de 98 pacientes con Alzheimer y 98 controles sanos del Pitt Corpus incluido en la base de datos Dementia-Bank. Un total de 1200 características de embebimientos de palabras, 1408 características de frecuencia de término inverso vs. frecuencia en documentos, y 8 características gramaticales fueron calculadas. Tres modelos fueron propuestos, basados en la extracción de dichos conjuntos de características por separado y un cuarto modelo fue basado en una estrategia de fusión temprana de los tres conjuntos de características. Los modelos fueron optimizados usando la estrategia de validación cruzada Leave-One-Out. Se alcanzaron tasas de aciertos de hasta 81.7 % usando la fusión temprana de todas las características. Además, se encontró que un pequeño conjunto de características gramaticales logró una tasa de acierto del 72.8 %. Así, los resultados indican que estas características son adecuadas para clasificar de manera efectiva entre pacientes de Alzheimer y controles sanos.es-ES
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dc.languageeng
dc.publisherInstituto Tecnológico Metropolitano (ITM)en-US
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1387/1456
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1387/1603
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1387/1591
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dc.rightsCopyright (c) 2019 TecnoLógicasen-US
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/4.0en-US
dc.sourceTecnoLógicas; Vol. 23 No. 47 (2020); 63-75en-US
dc.sourceTecnoLógicas; Vol. 23 Núm. 47 (2020); 63-75es-ES
dc.source2256-5337
dc.source0123-7799
dc.subjectAlzheimer's Diseaseen-US
dc.subjectNatural Language Processingen-US
dc.subjectText Miningen-US
dc.subjectClassificationen-US
dc.subjectMachine Learningen-US
dc.subjectEnfermedad de Alzheimeres-ES
dc.subjectprocesamiento de lenguaje naturales-ES
dc.subjectminería de textoes-ES
dc.subjectclasificaciónes-ES
dc.subjectaprendizaje de máquinaes-ES
dc.titleWord-Embeddings and Grammar Features to Detect Language Disorders in Alzheimer’s Disease Patientsen-US
dc.titleDetección de desórdenes de lenguaje de pacientes con enfermedad de Alzheimer usando embebimientos de palabras y características gramaticaleses-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeResearch Papersen-US
dc.typeArtículos de investigaciónes-ES


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