Invited talk at Univ. de Vigo (Ourense)

1. Multi-agent systems
applied in health care
Dr. Antonio Moreno
ITAKA-Intelligent Tech. for Advanced Knowledge Acquisition
Computer Science and Mathematics Dep.
Universitat Rovira i Virgili, Tarragona
http://deim.urv.cat/~itaka

2. Outline of the talk
Rationale for applying agents in health care
Some specific projects developed by the
members of ITAKA
Web-based platform for providing home
care services
Management of clinical guidelines
Research and development challenges
Final thoughts
http://deim.urv.cat/~itaka

3. Health Care problems
Distributed knowledge
E.g. different units of a hospital
Coordinated effort
E.g. receptionist, general and specialised
doctors, nurses, tests personnel, ...
Complex problems
E.g. home care management
Great amount of information
E.g. medical information in the Web

4. MAS applied in Health Care
Summary of main motivations
MAS are inherently distributed
Agents can coordinate their activities, while
keeping their autonomy and local data
Dynamic and flexible distributed problem solving
mechanisms
Use of personalisation techniques
Example: national organ transplant coordination

5. Growing interest
Specialised workshops at AA00, ECAI02,
ECAI04, IJCAI05, ECAI06, AAMAS08
AI-Communications special issues (2003, 2005)
Book on Whitestein Series on Agent
Technology (2007)
Int. Workshop on Health Care Applications
of Intelligent Agents – February 2003
Book on Whitestein Series on Agent
Technology (2003)
AI in Medicine special issue (2003)
IEEE Intelligent Systems special issue
(2006)

7. Outline of the talk
Rationale for applying agents in health care
Some specific projects developed by the
members of ITAKA
Web-based platform for providing home care
services
Management of clinical guidelines
Research and development challenges
Final thoughts

8. Knowledge-Based HomeCare eServices
for an Ageing Europe
Project Presentation
K4CARE Consortium
A Project funded by the European Community under the Sixth Framework Programme for Research and Technological Development
© K4Care, 2006 Contract no IST-026968

9. K4Care basic facts
March 2006 – March 2009 (3 years)
Extended until September 2009
EC funding: 3.130.000 €
STREP project, 6th FP
Thematic area: Information Society Technologies
(IST)
Coordinator: University Rovira i Virgili
13 Partners from 7 countries

10. K4Care project
The aim of the K4Care European project is to
provide a Home Care model, as well as design and
develop a prototype system, based on Web
technology and intelligent agents, that provides the
services defined in the model
Basic features:
a) Actors are members of well defined organizations, with
different roles and allowed activities
b) There is extensive domain knowledge to be considered
c) Coordination of tasks in daily care

11. K4Care Model: Structure
1 Nuclear Structure + n Accessory Services
THE K4CARE MODEL
...
HCNS
Actor Service
Data/Information
Action Procedure

12. K4Care Model: Actors and Teams

13. K4Care Knowledge structures
EHCR: Electronic Health Care Record
APO: Actor Profile Ontology
CPO: Case Profile Ontology
FIP: Formal Intervention Plan
Procedures
IIP: Individual Intervention Plan

14. DBs, Electronic Health Care Record
Data Base: with information about the
K4Care actors as users of the K4Care
Platform (e.g. contact information)
EHCR: with the data about the Home-
Care processes performed within the
K4Care Platform
Medical documents stored in XML

15. K4Care Ontologies (I)
Actor Profile Ontology (APO)
Types of actors
Actions related to each role
Platform services
Procedures
Documents
...

18. K4Care Ontologies (II)
Case Profile Ontology (CPO)
Diseases
Syndromes
Signs and symptoms
Social issues
Assessment tests
Interventions
...

21. K4Care FIPs
Formal Intervention Plans (FIPs) are formal
structures representing the health care
procedures to assist patients suffering form
particular ailments or diseases
FIPs are represented with the SDA* formalism
States
Decisions
Actions
The SDA* formalism is used to represent
K4CARE Service Procedures
K4CARE Formal Intervention Plans
K4CARE Individual Intervention Plans

22. FIP for the
management
of
hypertension

23. Procedures
Formal specifications, in the SDA* language,
of the way in which an administrative service
(e.g. admit a new patient to the Home Care
service) has to be implemented

24. Knowledge layer

25. Definition of an
Individual Intervention Plan
Input: patient data (EHCR), result of
comprehensive assessment, general K4Care
knowledge structures (APO, CPO, FIPs)
Output: Individual Intervention Plan to be
applied on a patient
Process:
Select set of applicable FIPs (diseases, syndroms,
symptoms)
Merge FIPs
Adapt the resulting SDA* structure to the individual
characteristics of the patient

28. K4Care platform features
Agent-based Web-accessible platform that
provides a set of basic Home Care services
Definition of IIPs
Apply IIP to the patient
Relevant aspects
Declarative and procedural knowledge
Separation of the knowledge description from the
software realization
Interaction between agents and end-users
Agent-oriented execution of patient-centred plans

29. Transparency between knowledge and
its use

30. Interaction between agents and users

31. Multi-agent system
1 Actor Agent for each user, permanently
running
When the user logs in, a Gateway Agent is
dynamically created
Two-way communication Web-servlet-GA-AA
When an Actor Agent has to manage the
execution of a procedure/IIP, it creates
dynamically a SDA-executor Agent

32. Agent-based execution of IIPs (I)

33. Agent-based execution of IIPs (II)

34. Agent-based execution of IIPs (III)

35. Agent-based execution of IIPs (IV)

36. K4Care main conclusions
Knowledge
Individual Intervention Plans allow practitioners to
implement accurate and personalised sequences
of actions for a particular patient’s treatment
Use
The architecture allows implementing agent-
based coordination methods between the actors
relevant in Home Care, which adapt their
behaviour dynamically depending on the
knowledge available in the platform

37. Outline of the talk
Rationale for applying agents in health care
Some specific projects developed by the
members of ITAKA
Web-based platform for providing home care
services
Management of clinical guidelines
Research and development challenges
Final thoughts

38. AgentCities.NET
AgentCities: European project (finished in
October 2003) that aimed to build a
network of open agent-based systems that
provided intelligent services associated to a
city
Our problem: to allow citizens and visitors
of a city to have easy, flexible and secure
access to medical information

40. Features: HeCaSe
The user may request information about all
the medical centres available in a particular
city
It is possible to book a visit to a doctor
The user may access his Medical Record
The medical data about patients is protected
and only authorised people can access to it
(ciphered transmissions)

41. Clinical Guidelines (CGs)
Indications or principles to assist health
care practitioners with patient care
decisions
Applicable in diagnostic, therapeutic, or
other clinical procedures for specific
clinical circumstances

42. CGs: benefits
Consistent clinical practice, avoidance of
errors
Reutilisation and tailoring
Rapid dissemination of updates and changes
Consideration of appropriate knowledge at
appropriate time
Use of formal representation languages

43. CGs: barriers in daily use
Lack of awareness
Lack of familiarity Automatic
Inertia of previous management and
behaviours enactment of
No integration with guidelines
standard practices
Lack of time or resources

44. Features: HeCaSe2
Medical services have been included
It is possible to coordinate complex tasks
between doctors and services, e.g. booking
different services following some constraints
Doctors can take decisions taking into
account all the CG’s related results
Repository of guidelines
Medico-organisational ontology

46. Knowledge representation
46
Separate the medical and organizational
knowledge from its actual use, to improve flexibility
and adaptability to different clinical settings
Two different ways of representing knowledge:
Procedural: contained in CGs
know-how
Declarative: external to CGs and managed through
an application ontology
know-what

47. Medico-organizational ontology
47
Create an application ontology for:
a) Modelling the healthcare entities with their
relations
b) Representing all medical terminology used by
all partners
c) Assigning semantic categories to those
medical concepts

48. Medical ontology: main classes
48
Main classes:
a) Agent class (information about internal
organisation)
Information about Departments, Practitioners,
Medical centres, Patient
b) Medical_domain (medical terms)
Diseases, Personal data and Procedures
c) Entity and Event (semantic types, UMLS)
Term categories and activities

49. Available relations (i): Object properties
49
Object property Description
belongsTo Any instance of a class that belongs to another
hasAssociatedProcedure A medical concept has an associated procedure. It is
used by doctors to simplify a search (from UMLS)
hasResponsible Establish the responsibility of any medical concept that
has to be performed by a healthcare actor
hasSemanticType Functional property to specify the semantic type of a
concept
isAssociatedProcedureOf Inverse of hasAssociatedProcedure
isComposedBy If an instance a ∈ A belongsTo b ∈ B then, b ∈ B
isComposedBy a ∈ A.
isResponsibleOf Inverse of hasResponsible

50. Available relations (ii): Data type properties
50
Data type property Description
hasCUI Value of the CUI (Code Unique Identifier)
hasDescription Concept definition provided by UMLS
hasResult Type of output of an element (action or data concept)
hasResultBoolean Sub-class of hasResult that sets a Boolean as output
hasResultInteger Sub-class of hasResult that sets an Integer as output
hasResultString Sub-class of hasResult that sets a String as output
hasResultEnumerate Sub-class of hasResult that sets an Enumerate as
output
hasResultComplex Sub-class of hasResult that sets a complex element
formed by one or more simple results (concepts) as
output
hasTUI Type Unique Identifier (TUI) of the concept

52. Execution of CGs (i)
52
Retrieving the CG and patient’s data
The doctor gets the appropriate CG through the GA
At the same time, the DRA receives (proactively) the
patient’s data through the MRA
The DRA gets (iteratively) the next stage to follow
(decision, enquiry or action)
Decisions:
The DRA interprets the logical conditions included in a
decision
Current values of the required variables are stored in the
patients’ health record

53. Execution of CGs (ii)
53
Actions: the DRA should contact with the
responsible of performing the required action
If the DRA wants to know information about the biopsy
action
The DRA contacts with the OA that knows:
A biopsy is a diagnostic procedure
A biopsy is performed in a surgery department
A surgeon belongs to the surgery dept.
Finally, the DRA contacts with an available surgeon to
perform the biopsy

54. Execution of CGs (iii)
54
Enquiries: the DRA collects data about the
required variables
The enquiries (usually) are findings or properties that
are introduced by doctors or nurses
The DRA may require other specialists to get them
The OA gives the information about the appropriate
agent to contact

55. Agents for Clinical Guideline enactment

56. Outline of the talk
Rationale for applying agents in health care
Some specific projects developed by the
members of ITAKA
Web-based platform for providing home care
services
Management of clinical guidelines
Research and development challenges
Final thoughts

57. Some research topics on the use of
MAS in Health Care
Communication standards
Medical ontologies
Security mechanisms
Implementation of agents in mobile
devices
Personalised access to information
Less social and professional reluctance to
adopt agent technology
Legal issues

58. General research topics on MAS
Service description, discovery, composition
Standard agent communication languages
and protocols
Negotiation, coordination, cooperation
techniques
Agent-Oriented Software Engineering
Trust
Human-agent interaction
Integration with legacy software
...

59. Outline of the talk
Rationale for applying agents in health care
Some specific projects developed by the
members of ITAKA
Web-based platform for providing home care
services
Management of clinical guidelines
Research and development challenges
Final thoughts

60. Some general thoughts (I)
It is difficult to work with doctors
Very busy, unaware of technical details, change
requirements…
However, they may end up being happy with a
rather simple system (e.g. a well-organised DB,
statistics for annual report)
It is difficult to sell “agents” to hospital
computer units
Understanding, maintenance, …
Information systems are hospital-wide,
centralised

61. Some general thoughts (II)
Security is a matter of degree …
Sometimes “real life” technical issues make
it unsuitable to use agents
Use of previous prototypes or programming
languages
The frontier between “agents” and “non-
agents” seems to be difficult to define

62. Multi-agent systems
applied in health care
Dr. Antonio Moreno
ITAKA-Intelligent Tech. for Advanced Knowledge Acquisition
Computer Science and Mathematics Dep.
Universitat Rovira i Virgili, Tarragona
http://deim.urv.cat/~itaka