Automatically monitoring and supporting healthy lifestyle is a recent research trend, fostered by the availability of low-cost monitoring devices, and it can significantly contribute to the prevention of chronic diseases deriving from incorrect diet and lack of physical activity. In this talk I will present the HORUS.AI platform: an AI-based platform built upon the integration of semantic web technologies and persuasive techniques for motivating people to adopt healthy lifestyle or for supporting them to cope with the self-management of chronic diseases. The platform collects data from users’ devices, explicit users’ inputs, or from the external environment (e.g. facts of the world) and interacts with users by using a goal-based metaphor. Interactive dialogues are used for proposing set of challenges to users that, through a mobile application, are able to provide the required information and to receive contextual motivational messages helping them to achieve the proposed goals. HORUS.AI is constituted by two main layers: the Knowledge and the Dialog-Based Persuasive layers. The Knowledge Layer contains the knowledge bases modeling the specific domains for which users are monitored (e.g. diet), the rules provided by domain-experts, and the RDF-based reasoner that combines the modeled knowledge with the users’ generated data. The results produced by reasoning operations are coded into motivational strategies and messages by the Dialog-based Persuasive Layer. The Dialog-based Persuasive Layer creates and manages dialogues and generates motivational messages based on the information provided by the Knowledge Layer and learned from previous users’ behavior. This way, messages are tailored to specific users. These two layers are supported by an Input/Output Layer exploited for directly communicating with users (i.e. dedicated mobile application or social media channels) by providing summaries of the acquired data, the chat containing the interactions between the users and the system, and graphical items showing the users’ statuses with respect to their goals. HORUS.AI has been validated within the context of different territorial labs and projects and the observed results demonstrated the suitability of HORUS.AI in real-world scenarios.

1. Semantic AI for Healthcare
The HORUS.AI Platform
Mauro Dragoni
Fondazione Bruno Kessler
Process and Data Intelligence Research Unit
Health and Wellbeing High Impact Initiative
18th International Semantic Web Conference
Auckland, New Zealand
October 26th, 2019

2. 1 introduction
2 background & challenges
3 the HORUS.AI system
4 living labs and discussion
agenda

3. two words about myself
page
03
design of a real-time
reasoning strategy
Health &
Wellbeing
working on the
virtual coach project
definition of conceptual
models for Digital Health
FBK since
2011
a bit of business
processes
sentiment analysis
knowledge
management
Ph.D. in 2010
information retrieval
natural language
processing

4. our goal
page
04
To design a virtual coaching system supporting people
and physicians in real-time monitoring and
recommendation activities.

5. virtual coaching state of the art
page
05
horizontal
strategies
behavior change persuasive models barriers management
vertical
strategies
living
labs
specific
problem
use of
canned
text
fixed
intervention

6. challenges
page
06
 C1: to design a strategy that is flexible.

7. AI and health
page
07
medical imaging
AI strategies applied to the
analysis of x-rays, MRI, etc..
genomics
Application of data science
techniques for analyzing the
interactions between drugs and
genetic profiles.
diagnosis
Use of intelligent agents for
understanding the health
status of people.
chronic diseases
Supporting people in managing
their chronic diseases with
intelligent solutions.
personalized medicine
Design of AI-based solutions
tailored with patients history and
profile.
prevention
Monitoring people for avoid the
onset or the worsen of specific
diseases.

8. AI and virtual coaching
page
08
chronic diseases
Supporting people in managing
their chronic diseases with
intelligent solutions.
personalized medicine
Design of AI-based solutions
tailored with patients history and
profile.
prevention
Monitoring people for avoid the
onset or the worsen of specific
diseases.
AI Models

9. challenges
page
09
 C1: to design a strategy that is flexible.
 C2: to provide a knowledge-driven explainable model.

10. the persuasive framework
page
010
Prescription Persuasion
Guidelines-
based system
Coaching system

11. the persuasive framework
page
011
Harm op den Akker et al.: Tailored motivational message generation: A model and practical framework for real-time physical activity coaching.
Journal of Biomedical Informatics 55: 104-115 (2015)

12. challenges
page
012
 C1: to design a strategy that is flexible.
 C2: to provide a knowledge-driven explainable model.
 C3: to integrate the psychological dimension.

13. engaging users
page
013

14. challenges
page
014
 C1: to design a strategy that is flexible.
 C2: to provide a knowledge-driven explainable model.
 C3: to integrate the psychological dimension.
 C4: to reduce users effort.

15. the Semantic XAI paradigm
page
015
To understand the status
and the goals of a user.
To aggregate and analyze data
and knowledge coming from:
- users;
- experts;
- domain.
To support an engaging
communication with users
and physicians through
time.

16. the Semantic XAI paradigm
sense
page
016
structured data (patients’ logs, sensors, images, etc.).
task-oriented conversational agents.
interactions with closed answers.
application of NLP and IE strategies to open answers.
application of NLU strategies to open answers.
chat-bots supporting argumentation.

17. the Semantic XAI paradigm
think
page
017
definition of the supporting conceptual model.
design of real-time reasoning strategies.
integration of external knowledge and data sources.
synergy with external machine learning models.

18. the Semantic XAI paradigm
act
page
018
use of canned messages.
dynamic generation of messages.
support for multi-modal feedback.
integration of full-fledged behavior change policies.

19. our virtual coach history
page
019
21 3 4
2015: the idea
We started to discuss which were the
requirements of an effective virtual
coaching system and to design the first
prototype.
2016: first living lab
The prototype of the virtual coaching
system was used within a living lab on
Workplace Health Promotion
2017: first deployment
The virtual coaching platform was
deployed in the context of the Trentino
Salute 4.0 framework and used, through a
mobile application, by more than 4,000
users.
2018: evolution
The virtual coaching platform has been
partially redesigned for making it more
flexible and connectable from third-
parties.
2019: HORUS.AI

20. the HORUS.AI platform
page
020

21. the HORUS.AI platform
the knowledge layer
page
021

22. the knowledge layer
reasoner
page
022
 Two kind of entailment rules:
• Event-Based Rules (EB-Rules): evaluated in real-time after a user event is sent by users;
• Timespan-Based Rules (TB-Rules): evaluated in background on specific timestamp basis.
 Aspects of interest:
• non-monotonic reasoning;
• generation of new individuals;
• per-user reasoning.

23. the knowledge layer
the reasoning process
page
023

24. the knowledge layer
population of the knowledge base with undesired behaviors
page
024

25. the HORUS.AI platform
the persuasive layer
page
025

26. the persuasive layer
message generator process
page
026
Violation
Read
Feedback
Generation
Argument
Extraction
Suggestion
Integration

27. the persuasive layer
violation bean
page
027
Violation
Read
Feedback
Generation
Argument
Extraction
Suggestion
Integration

28. the persuasive layer
feedback generation
page
028
Violation
Read
Feedback
Generation
Argument
Extraction
Suggestion
Integration
feedback := temporal adverb + feed verb + adjective + quantity + food entity
Language
Persuasive
strategy

29. the persuasive layer
argument extraction
page
029
Violation
Read
Feedback
Generation
Argument
Extraction
Suggestion
Integration
argument := intro + food entity category + verb adjective + food property + consequent verb + consequence
Language
Persuasive
strategy

30. the persuasive layer
suggestion integration
page
030
Violation
Read
Feedback
Generation
Argument
Extraction
Suggestion
Integration
suggestion := introduction + food entity + alternative
Language
Persuasive
strategy

31. living lab
page
031
 The Key To Health project.
 120 users have been monitored for 7 weeks.
• 92 users in the intervention group;
• 28 users in the control group.
 We observed and reported the effectiveness of generated explanations.

32. evaluation
page
032
 Three kind of entailment
rules:
• QB-Rules (EB-Rules)
• DAY-RULES and WEEK-
RULES (TB-Rules)

33. challenges addressed
page
033
 C1: to design a strategy that is flexible.
 C2: to provide a knowledge-driven explainable model.
 C3: to integrate the psychological dimension.
 C4: to reduce users effort.

34. future work
page
034
 Extension of the knowledge base.
 Integration of HORUS.AI in more living labs.
 Investigation about the implementation of stream reasoning strategies.
 Extension of the reasoner with fuzzy capabilities.
 Learning monitoring rules adaptation by means of learning techniques.

35. contacts
page
035
Mauro Dragoni
dragoni@fbk.eu

36. thank you.