  Mild cognitive impairment (MCI) is a prodromal phase in the progression from
normal aging to dementia, especially Alzheimers disease. Even though there is
mild cognitive decline in MCI patients, they have normal overall cognition and
thus is challenging to distinguish from normal aging. Using transcribed data
obtained from recorded conversational interactions between participants and
trained interviewers, and applying supervised learning models to these data, a
recent clinical trial has shown a promising result in differentiating MCI from
normal aging. However, the substantial amount of interactions with medical
staff can still incur significant medical care expenses in practice. In this
paper, we propose a novel reinforcement learning (RL) framework to train an
efficient dialogue agent on existing transcripts from clinical trials.
Specifically, the agent is trained to sketch disease-specific lexical
probability distribution, and thus to converse in a way that maximizes the
diagnosis accuracy and minimizes the number of conversation turns. We evaluate
the performance of the proposed reinforcement learning framework on the MCI
diagnosis from a real clinical trial. The results show that while using only a
few turns of conversation, our framework can significantly outperform
state-of-the-art supervised learning approaches.
