Abstract
The search for early biomarkers of mild cognitive impairment (MCI) has been central to the Alzheimer’s Disease (AD) and dementia research community in recent years. To identify MCI status at the earliest possible point, recent studies have shown that linguistic markers such as word choice, utterance and sentence structures can potentially serve as preclinical behavioral markers. Here we present an adaptive dialogue algorithm (an AI-enabled dialogue agent) to identify sequences of questions (a dialogue policy) that distinguish MCI from normal (NL) cognitive status. Our AI agent adapts its questioning strategy based on the user’s previous responses to reach an individualized conversational strategy per user. Because the AI agent is adaptive and scales favorably with additional data, our method provides a potential avenue for large-scale preclinical screening of neurocognitive decline as a new digital biomarker, as well as longitudinal tracking of aging patterns in the outpatient setting.
Highlights
The search for early biomarkers of mild cognitive impairment (MCI) has been central to Alzheimer’s Disease (AD) and dementia research community in recent years
We introduce a data-driven method for developing an automated and scalable diagnostic screening tool for efficient detection of early MCI status based on linguistic data
Diagnostic predictions in the medical domain are modeled as supervised learning problems, whereby diagnostic labels provided by physician experts are used to guide the modeling process
Summary
The search for early biomarkers of mild cognitive impairment (MCI) has been central to Alzheimer’s Disease (AD) and dementia research community in recent years. Recent studies have shown that simple linguistic markers such as word choice, phrasing (i.e., “utterance”) and short speech patterns possess predictive power in assessing MCI status in the elderly population[9]. Note that this is quite different from “speech markers” that involve auditory changes in pronunciations[10,11,12] which reflect early symptomatic changes in speech generation. We develop a prototype AI dialogue agent that conducts screening conversations with participants This AI agent must learn to ask the specific sequence of questions that are more likely to elicit responses containing linguistic markers that distinguish MCI form normal (NL) aging. Results demonstrate that such an approach provides a potential avenue for longitudinal tracking of aging patterns through strategic and data-driven dialogue
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