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The focus of the proposed project is to enable automated detection and analysis of episodes of unexpected lucidity in individuals with late-stage dementia in which the individual long thought to have succumbed to dementia and lost most of his or her cognitive abilities temporarily regains the ability to communicate in a clear and coherent fashion. Currently, the evidence for the existence of these episodes is mostly anecdotal, stemming from reports by caregivers and healthcare professionals. According to these reports, clear speech and language are the most prominent features of episodes of cognitive lucidity. The very low frequency and unexpected nature of these episodes make it challenging to capture objective evidence in the form of audio or video recordings of these events needed to enable systematic and comprehensive investigations. Thus, it is necessary to develop technological solutions for automated linguistic analysis that can be used for long-term continuous monitoring of individuals in late stages of dementia. In this feasibility project, we will develop technology to address two challenging issues: a) accurate conversion of continuous speech to text, and b) automated analysis of the text to measure the degree of coherence. Without robust solutions for these problems, our ability to detect and fully capture and analyze coherent speech in a long-term monitoring setting will remain limited. We will address these problems by developing and testing a robust automatic speech recognition solution based on deep learning technology that can operate autonomously (without sending data to external servers). We will also adapt existing and develop new measures of semantic coherence that are able to work on imperfect transcripts resulting from automatic speech recognition. In order to develop and validate these tools and approaches, we will use existing datasets of spontaneous conversational speech by persons with mild and moderate dementia as well as healthy controls available as part of the Carolina Conversations Collection and Dementia Bank.
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DeconDTN: Deconfounding Deep Transformer Networks for Clinical NLP
  • 批准号:
    10626888
  • 项目类别:
  • 资助金额:
    $34.2万
  • 财政年份:
    2022
  • 负责人:
    Trevor Cohen
  • 依托单位:
Professional to Plain Language Neural Translation: A Path Toward Actionable Health Information
  • 批准号:
    10349319
  • 项目类别:
  • 资助金额:
    $19.04万
  • 财政年份:
    2022
  • 负责人:
    Trevor Cohen
  • 依托单位:
Professional to Plain Language Neural Translation: A Path Toward Actionable Health Information
  • 批准号:
    10579898
  • 项目类别:
  • 资助金额:
    $21.16万
  • 财政年份:
    2022
  • 负责人:
    Trevor Cohen
  • 依托单位:
DeconDTN: Deconfounding Deep Transformer Networks for Clinical NLP
  • 批准号:
    10467107
  • 项目类别:
  • 资助金额:
    $34.53万
  • 财政年份:
    2022
  • 负责人:
    Trevor Cohen
  • 依托单位:
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