Computerized assessment of linguistic indicators of lucidity in Alzheimer's Disease dementia

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Project Summary The focus of the parent project (AG069792) 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. Towards this goal, this project aims to develop a) technology for automatic conversion of speech produced by patients with dementia to text and b) measures of semantic coherence from the transcribed text. The development of this technology relies on state-of-the-art artificial intelligence (AI) and machine learning (ML) methods including deep learning and time series analysis. We are currently developing these approaches using existing datasets including the Carolina Conversations Corpus (CCC), Wisconsin Longitudinal Study (WLS) and Dementia Bank (DB) which contain audio and text transcripts of conversational interviews with patients with dementia of varying severity and healthy controls. Currently, the researchers in the AI/ML community use these resources by following individually developed pre-processing procedures that are partially described in publications resulting from their work and ad hoc code made available in multiple repositories. The results obtained by various investigators can be difficult to compare because of individual differences in how the data were processed and prepared for ML experimentation. For the one-year supplement project, we propose to create an open-source platform consisting of tools that will ingest original data available from DB, WLS, and CCC datasets and convert them to be AI/ML-ready in keeping with the current best practices in the AI/ML community.