Difference between revisions of "KHealth Chatbots"

Revision as of 03:08, 16 July 2019

Motivation and Background

Chatbot for Asthma management

kBot is a knowledge-driven personalized chatbot system designed to continuously track medication adherence of pediatric asthmatic patients (age 8 to 15) and monitor relevant health and environmental data. The outcome is to help asthma patients self manage their asthma progression by generating trigger alerts and educate them with various self-management strategies. kBot takes the form of an Android application with a frontend chat interface capable of conversing both text and voice-based conversations and a backend cloud-based server application that handles data collection, processing, and dialogue management. The domain knowledge component is pieced together from the Asthma and Allergy Foundation of America, Mayoclinic, and Verywell Health as well as our clinical collaborator. Whereas, the personalization aspect is derived from the patient’s history of asthma collected from the questionnaires and day-to-day conversations. The system has been evaluated by eight asthma clinicians and eight computer science researchers for chatbot quality, technology acceptance, and system usability. kBOT achieved an overall technology acceptance score of greater than 8 on an 11-point Likert scale and a mean System Usability Score (SUS) greater than 80 from both evaluation groups.t for Asthma

For detailed information visit - kBot wiki page

• Members
  • Dipesh Kadariya
  • Revathy Venkataraman
  • Joey Yip

• Contact
  • Dipesh Kadariya

Chatbot for depression

ReaCTrack is the acronym for Personalized Adverse Reaction Conversational-based Tracker for Clinical Depression. This is an interdisciplinary project of applying conversational Artificial Intelligence (AI) for personalized healthcare. The objective of ReaCTrack is to monitor and keep track of personal well-being and depressive symptoms of patients diagnosed with mental health disorders, with the overall goal of delivering personalized and efficient behavioral or medical interventions. It is a spin-off project of Depression.Through different analysis using machine learning (ML) and natural language processing (NLP), ReaCTrack extracts relevant information that is crucial to address the following issues:

1. Real-time monitoring of patients’ medication adherence and stores adherence record
2. Tracks mood change of depressed patients
3. Assesses medication effectiveness on a particular patient
4. Detects potential ADRs of antidepressant drugs

For detailed information visit - ReaCTrack wiki page

• Members
  • Joey Yip
  • Dipesh Kadariya
  • Soonjye Kho

• Contact
  • Joey Yip

Chatbot for Nutrition

Obesity is a common yet disastrous lifestyle disease worldwide. It is one of the leading cause of the overall global burden of disease. The number of calories contained in food is not always intuitive to humans and are responsible for the increased prevalence of obesity. There is a need to prevent overweight transitioning from obesity and minimizing obesity-induced hospitalization. While there exists several mobile and web applications that attempt to bridge this gap, they are either not user-friendly and ubiquitous for the day-to-day use by the general public. Increased adoption and ubiquity of mobile health (mHealth) technology such as conversational systems (chatbot) can be proposed to assist in such situations. Intelligent user-chatbot interactions are speculated to have an empowering effect for users to maintain their healthy lifestyle. We developed NOURICH, an interdisciplinary project that integrates food knowledge and their nutritional values to augment diet planning for personalized health. It is first of a kind voice assistant and its novelty includes leveraging two knowledge graphs (domain-specific and personalized user-centric) to (i) provide long-term tracking of food log, diet adherence, and daily calorie intake in a ubiquitous manner and (ii) identify diet habits of the users through images and incorporate them to profile the users to deliver personalized and effective dieting guidelines. We also discussed on how this user-centric voice assistant can be used to augment personalized healthcare for (i) self-monitoring, (ii) self-appraisal, (iii) self-management, (iv) intervention, and (v) disease progression tracking and tracking.

For detailed information visit - Nourich wiki page

• Members
  • Revathy Venkataraman
  • Joey Yip
  • Thilini Wijesiriwardene

• Contact
  • Revathy Venkataraman

Keynote talks

DEEP-DIAL @ AAAI 2019, Honolulu, 27 Feb 2019

Brief video of kHealth Chatbots

Publications

References