TY - GEN
T1 - Automatic prediction of linguistic decline in writings of subjects with degenerative dementia
AU - Davy, Weissenbacher
AU - Travis, Johnson A.
AU - Laura, Wojtulewicz
AU - Amylou, Dueck
AU - Dona, Locke
AU - Richard, Caselli
AU - Graciela, Gonzalez
N1 - Publisher Copyright:
©2016 Association for Computational Linguistics.
PY - 2016
Y1 - 2016
N2 - Given the limited success of medication in reversing the effects of Alzheimer's and other dementias, a lot of the neuroscience research has been focused on early detection, in order to slow the progress of the disease through different interventions. We propose a Natural Language Processing approach applied to descriptive writing to attempt to discriminate decline due to normal aging from decline due to predementia conditions. Within the context of a longitudinal study on Alzheimer's disease, we created a unique corpus of 201 descriptions of a control image written by subjects of the study. Our classifier, computing linguistic features, was able to discriminate normal from cognitively impaired patients to an accuracy of 86.1% using lexical and semantic irregularities found in their writing. This is a promising result towards elucidating the existence of a general pattern in linguistic deterioration caused by dementia that might be detectable from a subject's written descriptive language.
AB - Given the limited success of medication in reversing the effects of Alzheimer's and other dementias, a lot of the neuroscience research has been focused on early detection, in order to slow the progress of the disease through different interventions. We propose a Natural Language Processing approach applied to descriptive writing to attempt to discriminate decline due to normal aging from decline due to predementia conditions. Within the context of a longitudinal study on Alzheimer's disease, we created a unique corpus of 201 descriptions of a control image written by subjects of the study. Our classifier, computing linguistic features, was able to discriminate normal from cognitively impaired patients to an accuracy of 86.1% using lexical and semantic irregularities found in their writing. This is a promising result towards elucidating the existence of a general pattern in linguistic deterioration caused by dementia that might be detectable from a subject's written descriptive language.
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M3 - Conference contribution
AN - SCOPUS:84994075043
T3 - 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016 - Proceedings of the Conference
SP - 1198
EP - 1207
BT - 2016 Conference of the North American Chapter of the Association for Computational Linguistics
PB - Association for Computational Linguistics (ACL)
T2 - 15th Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016
Y2 - 12 June 2016 through 17 June 2016
ER -