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DTSTART:20191103T020000
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UID:calendar.360716.field_event_date.0@www.wright.edu
DTSTAMP:20260220T000908Z
CREATED:20190619T202427Z
DESCRIPTION:Committee:  Drs. Amit Sheth\, Advisor\, Krishnaprasad Thirunara
 yan\, Valerie Shalin (Department of Psychology)\, and Maninder Kalra\, MD\
 , PhD (Department of Pediatrics)ABSTRACT:Asthma\, a chronic pulmonary dise
 ase\, is one of the major health issues in the United States. Given its ch
 ronic nature\, the demand for continuous monitoring of patient’s adherence
  to the medication care plan\, assessment of their environmental triggers\
 , and management of asthma control level can be challenging in traditional
  clinical settings and taxing on clinical professionals. A shift from a re
 active to proactive asthma care can improve health outcomes and reduce exp
 enses. On the technology spectrum\, smart conversational systems and Inter
 net-of-Things (IoTs) are rapidly gaining popularity in the healthcare indu
 stry. By leveraging such technological prevalence\, it is feasible to desi
 gn a system that is capable of monitoring asthmatic patients for a prolong
 ed period and empowering them to manage their health better.In this thesis
 \, we describe kBot\, a knowledge-driven personalized chatbot system desig
 ned to continuously track medication adherence of pediatric asthmatic pati
 ents (age 8 to 15) and monitor relevant health and environmental data. The
  outcome is to help asthma patients self manage their asthma progression b
 y 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 conversatio
 ns and a backend cloud-based server application that handles data collecti
 on\, processing\, and dialogue management. The domain knowledge component 
 is pieced together from the Asthma and Allergy Foundation of America\, May
 oclinic\, and Verywell Health as well as our clinical collaborator. Wherea
 s\, the personalization aspect is derived from the patient’s history of as
 thma collected from the questionnaires and day-to-day conversations. The s
 ystem has been evaluated by eight asthma clinicians and eight computer sci
 ence 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.
DTSTART;TZID=America/New_York:20190624T103000
DTEND;TZID=America/New_York:20190624T122000
LAST-MODIFIED:20190620T181044Z
LOCATION:366 Joshi
SUMMARY:Masters Thesis Defense “kBot: Knowledge-Enabled Personalized Chatbo
 t for Self-Management of Asthma in Pediatric Population” By Dipesh Kadariy
 a
URL;TYPE=URI:/events/masters-thesis-defense-%E2%80%9C
 kbot-knowledge-enabled-personalized-chatbot-self-management-asthma
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