Topic-Based Measures of Conversation for Detecting Mild Cognitive Impairment.
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ABSTRACT:
SUBMITTER: Chen L
PROVIDER: S-EPMC7909094 | biostudies-literature | 2020 Jul
REPOSITORIES: biostudies-literature
Publications
Topic-Based Measures of Conversation for Detecting Mild Cognitive Impairment.
Chen Liu L Dodge Hiroko H HH Asgari Meysam M
Proceedings of the conference. Association for Computational Linguistics. Meeting 20200701
Conversation is a complex cognitive task that engages multiple aspects of cognitive functions to remember the discussed topics, monitor the semantic and linguistic elements, and recognize others' emotions. In this paper, we propose a computational method based on the lexical coherence of consecutive utterances to quantify topical variations in semi-structured conversations of older adults with cognitive impairments. Extracting the lexical knowledge of conversational utterances, our method genera ...[more]
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Project description:Abstract Studies have shown that speech characteristics can aid in early-identification of those with mild cognitive impairment (MCI). We performed a linguistic analysis on spoken utterances of 41 participants (15 MCI, 26 healthy controls) from conversations with a trained interviewer using the Term Frequency-Inverse Document Frequency (TF-IDF) method. Data came from a randomized controlled behavioral clinical trial (ClinicalTrials.gov: NCT01571427) to examine effects of conversation-based cognitive stimulation on cognitive functions among older adults with normal cognition or MCI, which served as a pilot study for I-CONECT. From the collected spoken utterances we first constructed a fixed-dimensional feature vector using TF-IDF. Next, to distinguish between MCI and healthy controls, we trained a support vector machine (SVM) classifier on per-subject feature vectors according to 5-fold cross-validation procedure. Our results verify the effectiveness of TF-IDF features in this classification task with Receiver Operating Characteristic Area Under Curve of 81%, well above chance at 65%.
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Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2015-08-05 | E-GEOD-63063 | biostudies-arrayexpress
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