[2A1-GS-10] AI application: Medicine / Healthcare

[2A1-GS-10-02] Initial study on agents that assist health guidance counselors towards the prevention of lifestyle-related diseases

-Modeling Efficient Question and Intervention Selection through Multitask Reinforcement Learning-

〇Taiga Sano¹, Kaori Fujimura¹, Tae Sato¹, Masami Takahashi¹, Masahiro Kohjima², Yurika Katagiri¹ (1. NTT Social Informatics Laboratories, 2. NTT Human Informatics Laboratories)

Keywords:Behavior change, Reinforcement learning, Healthcare

To reduce the risk of lifestyle-related diseases among the Japanese population, there is a health guidance program to motivate people to improve their lifestyles through interviews conducted by public health nurses, dietitians, and other healthcare professionals. However, differences in the biological, psychological, and social characteristics of the interviewees make motivation difficult within a limited interview time, even for skilled interviewers. We are constructing an agent that can assist the interviewers by providing them with appropriate topics (questions) and suggesting lifestyle modifications that will best motivate the interviewee. Multitask reinforcement learning is used to avoid questions that would not suggest lifestyle improvement measures and to select only questions that are necessary for understanding the interviewee's characteristics and for suggesting improvement measures. To validate our initial version of the agent, we tested it in a simple simulation environment and confirmed that the proposed method is more effective than comparable methods.