@inproceedings{asgari-etal-2020-topic,
    title = "Topic-Based Measures of Conversation for Detecting Mild {C}ognitive{I}mpairment",
    author = "Asgari, Meysam  and
      Chen, Liu  and
      Dodge, Hiroko",
    editor = "Bhatia, Parminder  and
      Lin, Steven  and
      Gangadharaiah, Rashmi  and
      Wallace, Byron  and
      Shafran, Izhak  and
      Shivade, Chaitanya  and
      Du, Nan  and
      Diab, Mona",
    booktitle = "Proceedings of the First Workshop on Natural Language Processing for Medical Conversations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.nlpmc-1.9/",
    doi = "10.18653/v1/2020.nlpmc-1.9",
    pages = "63--67",
    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."
}