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Knowledge enabled personalized chat-bot system for self-management of asthma in padiatric population (age 8-15).
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Dipesh Kadariya
Contents
Motivation
Asthma is a lung inflammatory disease that has prevailed more than 26 million people in the United States, out of which 6.1 million are children [ref]. It is the most chronic condition among the pediatric population[ref] accounting for 13.8 million missed school days each year [ref]. Though incurable, asthma can be controlled and managed by strict adherence to a medication care plan and by avoiding the triggers [ref]. However, due to a lack of consistent adherence to the asthma care plan, and inadequate information about the patient’s environment, asthma management can be challenging [ref]. These issues are further compounded by its multi-factorial nature, where every patient is sensitive to different triggers, and react differently even when exposed to the same trigger [ref]. This demands personalized care beyond the regular hospital setup to which the clinical professionals are limited. The advent of digital health monitoring technologies such as smart wearables and increasing adoption of mobile devices and low-cost sensors translate to an unprecedented amount of data being generated and collected on a continuous basis. It is now possible to monitor long-term medication adherence, exposure to environmental triggers, and asthma control level in real-time.
Challenges
- Continuous monitoring of the patient’s asthmatic condition requires their active involvement in the process. However, due to the static user interface of current monitoring systems, poor patients compliance badly affects the quality and quantity of data collection.
- In asthma care, the effectiveness of treatment is affected not only by patient compliance but also by the inhalers usage techniques. If asthma inhalers are not used in a proper way, it has no effect on patients asthmatic condition which may lead to worsening of asthma. For the effectiveness of asthma treatment, patients need to be educated on proper inhaler techniques and encouraged towards medical compliance.
- Asthma is a multi-factorial disease; each patient reacts to both triggers and treatment differently. A general treatment approach such as a generic asthma care plan proves to be ineffective in such a scenario. A more personalized approach to monitor patients and their environmental factors are required.
Opportunity
Recent years have seen immense maturity in Artificial Intelligence (AI) research which has in part proliferated the growth of intelligent conversational systems, also known as chatbots. They are increasingly popular due to their capability of simulating human-like conversations with a user through speech, text, smart display, and multimodal communication [ref]. Contrary to static applications, it can understand user intents and choices through interactions and communicate accordingly. An exciting trend is that chatbot-assisted queries are 200 times more conversational than search, and users are demanding more human-style interaction [ref]. Such technology is rapidly gaining traction in the healthcare domain where professional care is limited [ref]. It is evident that current long-term healthcare monitoring demands a more ubiquitous solution. Chatbots are capable of delivering a more convenient and accessible approach through cost-effective mediums. Nonetheless, they are limited in their inherent ability to contextualize, learn interactively, and provide the proper hyper-personalization needed to hold a meaningful conversation.
By leveraging such technology and considering the challenges mentioned earlier, we introduce a knowledge-enabled personalized chatbot system kBot, that is intended to replace our use of the mobile app in the kHealth Framework. It is capable of interacting with a patient through a contextualized and personalized manner on hand-held devices (mobile phones and tablets), or as part of smart displays (e.g., Google Home Hub). It curates and contextualizes its asthma domain knowledge from different online sources such as Asthma and Allergy Foundation of America (AAFA) [ref], verywell health [ref], Mayo clinic [ref], and webMD [ref] as well as local inputs from our clinical collaborators. It then aggregates this knowledge with patients’ data such as symptoms and medication intake to deliver a personalized conversation experience. The first version of kBot reported here takes on a design approach that centers around addressing medication nonadherence issue in pediatric asthma management and assessing environmental triggers at an individual level. The ultimate goal is to bridge and simplify long-term real-time monitoring of asthma condition, alert on potential environmental triggers, and educate the patients on various asthma self-management skills. Our current implementation is limited to the Android ecosystem but can be easily adapted to iOS.
Evaluation
Architecture
Contextualization
Personalization
Video
Team Members
Dipesh Kadariya, Revathy Venkataramanan, and Kirill Kultinov
Related Publications
- Dipesh Kadariya, Revathy Venkataramanan, Hong Yung Yip, Maninder Kalra, Thirunarayan Krishnaprasad, Amit Sheth. "kBot: Knowledge-enabled Personalized Chatbot for Asthma Self-Management". In Proceedings of the IEEE SMARTSYS Workshop on Smart Service Systems (SMARTCOMP 2019). IEEE, 2019.
Funding
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