k-BOT: Knowledge-driven Chatbot for Health

1. Collaborative Assistants for the Society (CASY 2020)
k-BOT: Knowledge-driven Chatbot
for Health
Hong Yung (Joey) Yip
PhD Student @ Artificial Intelligence Institute, UofSC (AIISC)
October 16th, 2020 @ University of South Carolina, Columbia, SC, USA

2. Paradigm Shift in Healthcare
● Episodic to Continuous Monitoring
● Clinician-centric to Patient-centric
● Clinician controlled to Patient-empowered
● Disease Focused to Wellness-focused
● Sparse data to Multimodal Big Data
Medical
Internet
of Things
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Images from: https://thenounproject.com

3. Augmented Personalised Health (APH)
From Big Data to Smart Data
Augmented Personalised Health (APH) is a vision to enhance the healthcare by using
AI techniques on semantically integrated Patient-Generated Health Data (PGHD),
environmental, clinical, public health & social data.
Data Components
PGHD, Clinical, Environmental,
and Social Data
Smart Data
Meaningful data after
contextualised processing
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http://wiki.aiisc.ai/index.php/Augmented_Personalized_Health:_How_Smart_Data_with_IoTs_and_AI_is_about_to_Change_Healthcare
Images from: https://thenounproject.com

4. kBOT for kHealth Asthma
Highly Diverse Data up to 29 parameters
(& collection methods: Active + Passive):
Up to 1852 data points/ patient /day
kBot with screen interface
for conversation
Text
Speech
● Smarter & engaging agent
● Minimize active sensing (Questions to be asked)
● Ask only informed & intelligent questions
● Relevant & Contextualized conversations
● Personalized & Human-Like
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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7432964/

5. kBOT Demo
5https://youtu.be/oZCaM7rhJAg
More on: https://www.slideshare.net/aiisc/kbot-knowledgeenabled-personalized-chatbot-for-selfmanagement-of-asthma-in-pediatric-population

6. Other Areas of Work & Takeaways
WHY
● Obesity is a common lifestyle disease worldwide
● Number of calories contained in food is not always intuitive
● Minimize obesity-induced hospitalization
Our approach
● Integrate food knowledge (domain-specific and personalized user-
centric) and their nutritional values to augment diet planning for
personalized health
● Support multimodal interactions (text, image, speech)
For Nutrition Tracking and Diet Monitoring Modeling Social Behavior for Healthcare Utilization in Depression
WHY
● Leading cause of disability worldwide
● $40 billion has been spent each year on depression treatment
● Effectiveness of treatment varies between different individuals
Our approach
● Integrate MedDRA, Drug Abuse Ontology (DAO), SIDER
● Monitor and keep track of personal well-being and depressive symptoms
● Deliver personalized and efficient behavioral or medical interventions
Mental Health (http://wiki.aiisc.ai/index.php/KHealth_Chatbots)Nutrition (http://wiki.aiisc.ai/index.php/Nourich)
Conversation systems are making it easy for users (patients) to
interact with and use technology.
Three most important features are
1. Personalization: It is all about you, it knows your history (not generic)
2. Contextualization: It understands your unique situation (domain knowledge)
3. Secure and private.
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Acknowledgement: Dipesh Kadariya, Revathy Venkataramanan, Maninder Kalra, Krishnaprasad Thirunarayanan, and Amit Sheth

7. Contextualization and
Personalization
kBOT initiates greeting
conversation.
Understands the patient’s health
condition (allergic reaction to high
ragweed pollen level) via the
personalized patient’s knowledge
graph generated from EMR, PGHD,
and prior interactions with the kBOT.
Generates predictions or
recommended course of actions.
Inference based on patient’s
historical records and background
health knowledge graph containing
contextualized (domain-specific)
knowledge.
Figure: Example kBOT conversation which
utilizes background health knowledge graph and
patient’s knowledge graph to infer and generate
recommendation to patients.
★ Conversing only information relevant to
the patient
Context enabled by relevant
healthcare knowledge including
clinical protocols.
On-going Work
(Snapshot)
7

8. 8
ONE SLIDE TO SHOW HOW
PHKG EVOLVES OVER TIME
kHealth Ontology API
APH kHealth Project (Pediatric Asthma)
Reasoning mechanisms
Enriching KG
Enriching KG
In-built rule-based
inference engine
Machine
Learning
Updating the KG
with more triples
Analyzing datasets
Executing reasoning
Ontology Catalogs:
● BioPortal
● Linked Open Vocabularies (LOV)
● Linked Open Vocabularies for
Internet of Things (LOV4IoT)
Linked Open Data (LOD):
● UMLS
● SNOMED-CT
● ICD-10
● Clinical Trials
● Sider
Personalized Health
Knowledge Graph (PHKG)
Personal
Sensor Data
Electronic Medical
Records (EMR)
Figure: Personalized Health Knowledge Graph
(Amelie et. al, 2019)
https://scholarcommons.sc.edu/aii_fac_pub/42/
Personalized Health Knowledge Graph

9. Sample Ontology
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Asthma Ontology (In-house)