A Digital Language Coherence Marker for Monitoring Dementia
This provides a non-intrusive, cost-effective tool for monitoring cognitive decline in dementia patients, though it appears incremental as it builds on existing digital marker approaches.
The authors tackled the problem of monitoring dementia by proposing a digital language coherence marker derived from spontaneous speech, which showed significant differences between healthy controls, mild cognitive impairment, and Alzheimer's Disease patients, with high association to clinical biomarkers.
The use of spontaneous language to derive appropriate digital markers has become an emergent, promising and non-intrusive method to diagnose and monitor dementia. Here we propose methods to capture language coherence as a cost-effective, human-interpretable digital marker for monitoring cognitive changes in people with dementia. We introduce a novel task to learn the temporal logical consistency of utterances in short transcribed narratives and investigate a range of neural approaches. We compare such language coherence patterns between people with dementia and healthy controls and conduct a longitudinal evaluation against three clinical bio-markers to investigate the reliability of our proposed digital coherence marker. The coherence marker shows a significant difference between people with mild cognitive impairment, those with Alzheimer's Disease and healthy controls. Moreover our analysis shows high association between the coherence marker and the clinical bio-markers as well as generalisability potential to other related conditions.