Topic-Based Measures of Conversation for Detecting Mild CognitiveImpairment

Meysam Asgari, Liu Chen, Hiroko Dodge


Abstract
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 generate a set of novel conversational measures that indicate underlying cognitive deficits among subjects with mild cognitive impairment (MCI). Our preliminary results verifies the utility of the proposed conversation-based measures in distinguishing MCI from healthy controls.
Anthology ID:
2020.nlpmc-1.9
Volume:
Proceedings of the First Workshop on Natural Language Processing for Medical Conversations
Month:
July
Year:
2020
Address:
Online
Venues:
ACL | NLPMC | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
63–67
Language:
URL:
https://www.aclweb.org/anthology/2020.nlpmc-1.9
DOI:
10.18653/v1/2020.nlpmc-1.9
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.nlpmc-1.9.pdf
Video:
 http://slideslive.com/38929894