A Grounded Well-being Conversational Agent with Multiple Interaction Modes: Preliminary Results (2021)

Abstract: Technologies for enhancing well-being, healthcare vigilance and monitoring are on the rise. However, despite patient interest, such technologies suffer from low adoption. One hypothesis for this limited adoption is loss of human interaction that is central to doctor-patient encounters. In this paper we seek to address this limitation via a conversational agent that adopts one aspect of in-person doctor-patient interactions: A human avatar to facilitate medical grounded question answering. This is akin to the in-person scenario where the doctor may point to the human body or the patient may point to their own body to express their conditions. Additionally, our agent has multiple interaction modes, that may give more options for the patient to use the agent, not just for medical question answering, but also to engage in conversations about general topics and current events. Both the avatar, and the multiple interaction modes could help improve adherence. We present a high level overview of the design of our agent, Marie Bot Wellbeing. We also report implementation details of our early prototype , and present preliminary results.

## introduction nlp is in a position to bring-forth scalable, costeffective solutions for promoting well-being. such solutions can serve many segments of the population such as people living in medically underserved communities with limited access to clinicians, and people with limited mobility. these solutions can also serve those interested in selfmonitoring @xcite ) their own health. there is evidence that these technologies can be effective @xcite . however, despite interest, such technologies suffer from low adoption @xcite . one hypothesis for this limited adoption is the loss of human interaction which is central to doctor-patient encounters @xcite . in this paper we seek to address this limitation via a conversational agent that emulates one aspect of in-person doctor-patient interactions: a human avatar to facilitate grounded question answering. this is akin to the in-person scenario where the doctor may point to the human body or the patient may point to their own body to express their conditions. additionally, our agent has multiple interaction modes, that may give more options for the patient to use the agent, not just for medical question answering, but also to engage in conversations about general topics and current events. both the avatar, and the multiple interaction modes could help improve adherence. the human body is complex and information about how it functions fill entire books. yet it is important for individuals to know about conditions that can affect the human body, in order to practice continued monitoring and prevention to keep severe medical situations at bay. to this end, our wellbeing agent includes a medical question answering interaction mode (medicalqabot). for mental health, social isolation and loneliness can have adverse health consequences such as anxiety, depression, and suicide. our well-being agent includes a social interaction mode (socialbot), wherein the agent can be an approximation of human a companion. the medicalqabot is less conversational but accomplishes the task of answering questions. the socialbot seeks to be conversational while providing some information. and, there is a third interaction mode, the chatbot, which in our work is used as a last-resort mode, it is conversational but does not provide much information of substance. to test the ideas of our proposed agent, we are developing a grounded well-being conversational agent, called "marie bot wellbeing". this paper presents a sketch of the high level design of our marie system, and some preliminary results. colon cancer typically affects older adults, though it can happen at any age. marie, who is at risk of getting colon cancer? which body parts are affected? colon cancer begins in the large intestine (colon) let me show you on my body … ance for errors. erroneous information can have severe negative consequences. we design the med-icalqabot, and the socialbot with this consideration in mind. our design philosophy consists of the following tenets: organization in what follows, we discuss how the above tenets are manifested in our agent. the rest of the paper is organized as follows: we begin with a high-level overview of the design of the different parts of the agent (sections 2 to 4); we next discuss the current prototype implementation and preliminary results (section 5); we next present related work (section 6); and close with a discussion (section 7) and concluding remarks (section 8). ## interaction modes and dialog management in navigating between the different interaction modes, we design our system as follows. based on the user utterance, we automatically predict using a binary classifier to switch between different interaction modes ( medicalqabot vs socialbot). suppose that the classifier predicts that the utterance is a question asking for medical information on a topic, and suppose our medicalqa determines that we have no information on that topic, our goal is to then let the socialbot take over if it has information on that topic and can meaningfully hold a conversation about it. for the socialbot, when missing the necessary information, our goal is to have it fall back to chatbot mode. their long, fine-textured coat requires considerable care and grooming. marie, i am thinking of getting an afghan hound. ok, but how about temperament? are they good with cats? this breed, as is the case with many sighthounds, has a high prey drive and may not get along with small animals. ## socialbot mode for the socialbot, we propose to create a knowledge vault of topics that will enable the bot to have engaging conversations with humans on topics of interest including current events. for example, the bot can say "sure, we can talk about german beer" or. "i see you want to talk about afghan hounds"". the topics will be mined from wikipedia, news sources, and social media including reddit. for the socialbot, we wish to model the principles of a good conversation: having something interesting to say, and showing interest in what the conversation partner says @xcite ## prototype implementation & preliminary experiments having discussed the high-level design goals, in the following sections we present specifics of our initial prototype. our prototype's language understanding capabilities are limited. they can be thought of as placeholders that allowed us to quickly develop a prototype. these simple capabilities will be replaced as we develop more advanced language processing methods for our system. ## conclusion we have presented a high level overview of the design philosophy of marie bot wellbeing, a grounded, multi-interaction mode well-being conversational agent. the agent is designed to mitigate the limited adoption that plagues agents for healthcare despite patient interest. we reported details of our prototype implementation, and preliminary results. there is much more to be done to fully realize marie, which is part of our ongoing work.