1. Extending computer guideline system with advanced AI and DB facilities
Alessio Bottrighi

2. Alessio Bottrighi 's Ph.d. Dissertation
A clinical guidelines is sistematically developments statemnts to assist pratictioners and patient decision about appropriate health care for specific clinical circumstance
Alessio Bottrighi 's Ph.d. Dissertation

3. Alessio Bottrighi 's Ph.d. Dissertation
Our work
Decision theory support
Model checking in clinical guidelines
Cooperative update
Alessio Bottrighi 's Ph.d. Dissertation

4. Decision System Support
decision making is a central issue in clinical practice.
it is frequent that it is not possible define if an alternative is really “better” than other, for a clinical point of view
Alessio Bottrighi 's Ph.d. Dissertation

5. Decision System Support: our work
We made a systematic analysis of main clinical guidelines representation primitives
We made the mapping of representation primitives into Markov Decision Process.
We studied how algorithms for evaluating utility and for evaluating the optimal policy can be exploited in clinical guidelines context
As case of study, we have applied our general approach to the GLARE system
Alessio Bottrighi 's Ph.d. Dissertation

6. Decision System Support: publication
S. Montani, P. Terenziani, A. Bottrighi, Exploiting decision theory for supporting therapy selection in computerized clinical guidelines, Proc. European Conference on Artificial Intelligence in Medicine (AIME) 2005, July 2005
P. Terenziani, S. Montani, A. Bottrighi, M. Torchio, G. Molino, G. Correndo, Managing clinical guidelines contextualization in the GLARE system, Proc. Congresso Nazionale dell'Associazione Italiana per l'Intelligenza Artificiale (AI*IA) 2005, September 2005
Alessio Bottrighi 's Ph.d. Dissertation

7. Model checking in clinical guidelines
improving the quality of clinical guideline
the capabilities verification available in the guideline systems is usually rather limited
defining a general mechanism to evaluate the quality of clinical guideline
Alessio Bottrighi 's Ph.d. Dissertation

8. Model checking in clinical guidelines: our work
We had mapped the behaviour of a clinical guideline into a formalism accepted by the model checker (the SPIN model checker)
We have defined a comprehensive framework in which a computer-based approach to clinical guidelines is developed using an agent-based technology
We have identified the types of properties that are useful to verify about a clinical guideline
As case of study, we have applied our general approach to the GLARE system
Alessio Bottrighi 's Ph.d. Dissertation

9. Model checking in clinical guidelines: publication
P. Terenziani, L. Giordano, A. Bottrighi, S. Montani, L.Donzella, Spin Model Ckecking for the verification of Clinical Guidelines, Workshop on AI techniques in healthcare: evidence-based guidelines and protocols, ECAI 2006, August 2006
L. Giordano, P. Terenziani, A. Bottrighi, S. Montani, L. Donzella, Model checking for clinical guidelines: an agent-based approach, American Medical Informatics Association (AMIA), November 2006
Alessio Bottrighi 's Ph.d. Dissertation

10. Cooperative Update: introduction (1)
Important, e.g. software development
Multiple alternative proposals
Selection
Software engineering tools
Analogous problems using Database to model complex domains
Alessio Bottrighi 's Ph.d. Dissertation

11. Cooperative Update: introduction (2)
The case of clinical guidelines:
General guideline proposed by a standardization committee
Proposals of update
Local contextualization
New therapies
Evaluation of proposals
Guideline to be stored in a DB
Alessio Bottrighi 's Ph.d. Dissertation

12. Cooperative Update: introduction (3)
Augmenting DB approaches to support cooperative work, i.e.:
distinction between two phases:
proposals and acceptance/rejection
history of the evolution of the proposals
alternative proposals
Alessio Bottrighi 's Ph.d. Dissertation

13. Cooperative Update: introduction (4)
Both validity time and transaction time should be supported
“Consensus” approach (TSQL2) with a high-level semantics (BCDM)
BCDM supports several Temporal Database implementations (not only TSQL2)
Alessio Bottrighi 's Ph.d. Dissertation

14. Cooperative Update: goal
Extending BCDM to support cooperative updates
Propose vs accept/reject
Alternative proposals of updates
Alessio Bottrighi 's Ph.d. Dissertation

15. Cooperative Update: criteria (1)
Under-constrained policy:
Super user vs user
Super user operations: standard + accept/reject proposals
User operations:
delete (not proposals)
insert
update (chains allowed)
Alessio Bottrighi 's Ph.d. Dissertation

16. Cooperative Update: criteria (2)
“Minimal” extension of BCDM:
Upward compatibility (manipulation operations)
Reducibility (algebra)
Alessio Bottrighi 's Ph.d. Dissertation

17. Cooperative Update: our work
Data model
Manipulation operations
Algebra
Alessio Bottrighi 's Ph.d. Dissertation

18. Cooperative Update: data model (1)
Two data levels needed:
Super users (accepted) data
User proposals
Observe that proposals need to be maintained and affect super-user data only if/when accepted
Alessio Bottrighi 's Ph.d. Dissertation

19. Cooperative Update: data model (2)
Authoring:
author as a data attribute
Basically a “standard” data attribute (however, author cannot be modified)
Alessio Bottrighi 's Ph.d. Dissertation

20. Cooperative Update: data model (3)
Super user data:
Standard BCDM semantics
User proposals data:
For each super-user relation r:
insert(r)
delete(r)
update(r)
Alessio Bottrighi 's Ph.d. Dissertation

21. Cooperative Update: data model (4)
insert(r) is a set of standard BCDM tuples
delete(r) is a set of standard transaction-time tuples
Alessio Bottrighi 's Ph.d. Dissertation

22. Cooperative Update: data model (5)
Update involves:
An origin tuple to be updated (time not needed)
A new temporal tuple (standard BCDM tuple)
Alessio Bottrighi 's Ph.d. Dissertation

23. Cooperative Update: data model (6)
Semantic interpretation: disjunctive set of alternative proposals (each one is a BCDM tuple)
Alessio Bottrighi 's Ph.d. Dissertation

24. Cooperative Update: data model (7)
Definition: proposal tuple pt = <o, Alt{alt1,alt2,..,altk}>
an origin o
a non empty set Alt{alt1,alt2,..,altk} of (bi)temporal tuples
Observe that Alt{alt1,alt2,..,altk} is a disjunctive set of mutually exclusive tuples referring to the origin o, representing the different proposals of update concerning o.
Alessio Bottrighi 's Ph.d. Dissertation

25. Cooperative Update: data model (8)
proposal(r) is a set of Proposal-tuples
Alessio Bottrighi 's Ph.d. Dissertation

26. Cooperative Update: manipulation operation (1)
User:
propose_update
propose_insert
propose_delete
Alessio Bottrighi 's Ph.d. Dissertation

27. Cooperative Update: manipulation operation (2)
Super-user:
accept_update, accept_insert, accept_delete
reject_update, reject_insert, reject_delete
confirm
Alessio Bottrighi 's Ph.d. Dissertation

28. Cooperative Update: manipulation operation (3)
E.g.: propose_update(r,origin,old,new,VT)
Alessio Bottrighi 's Ph.d. Dissertation

29. Cooperative Update: manipulation operation (4)
E.g.: propose_update(r,origin,old,new,VT)
IF admissible
IF  ptproposal(r) with origin(pt)=origin
THEN add <origin, <new,user,UCVT>> in proposal(r)
insert a new propoasal tuple
IF  ptproposal (r) with origin(pt)=origin 
( a1  alternatives(pt)\ a1 value equivalent to ‘new’ OR
 a1  alternatives(pt)\ a1 value equivalent to ‘new’ 
user(a) user)
THEN add ‘new’ to alternatives(pt)
insert a new proposal into an existing proposal-tuple
user(a) = user
THEN add (UCVT) to the bitemporal of a1
update previous proposal
Alessio Bottrighi 's Ph.d. Dissertation

30. Cooperative Update: manipulation operation (5)
Admissibility of propose_uptade operation
origin: in r or in insert(r)  current
old: old (old=origin OR old origin)  current
new: ( tuple t r  current  t value equivalent to ‘new’  t value equivalent to origin) 
 proposal value equivalent to t with same VT
Alessio Bottrighi 's Ph.d. Dissertation

31. Cooperative Update: manipulation operation (6)
Condition on ‘new’: example
r: {<a,u,Ta>,<b,u,Tb>,…..}
(r is a super-user relation)
Admissible update:
a,u  <a,u’,T’>
NOT admissible:
b,u  <a,u,T’>

32. Cooperative Update: manipulation operation (7)
E.g.: accept update proposal
IF admissible
IF  tr \ t value equivalent to origin  current(t)
THEN DELETE(t); INSERT(new);
close UC to all alternative proposals concerning origin
IF  tr \ t value equivalent to origin  current(t)   tinsert(r) \ t value equivalent to origin  current(t)
THEN INSERT(new);
close UC to all alternative proposals concerning origin
admissible:
 ptproposal(r) with origin(pt)=origin  newalternatives(pt)  current(new) 
[( tr \ t value equivalent to new  current(t))  t value equivalent to origin]
Alessio Bottrighi 's Ph.d. Dissertation

33. Cooperative Update: algebra(1)
Standard BCDM algebraic operations for super-user and for insert(r), delete(r)
New algebraic operations on Proposal-relations
Alessio Bottrighi 's Ph.d. Dissertation

34. Cooperative Update: algebra(2)
E.g.: natural join:
r⋈A s = { z=<origin(z),alternatives(z)> \
IF $pt1Îr , $pt2Îs \ origin(pt1)[A]= origin(pt2) [A] $a1Îalternatives(pt1), $a2Îalternatives(pt2) \ a1[A]=a2[A] Ù a1[T]a2[T] 
THEN
origin(z)[A]=origin(pt1)[A] Ù z[B]=origin(pt1)[B] Ù z[C]=origin(pt2)[C] Ù altÎalternatives(z),
where alt[A]=a1[A]=a2[A] Ù alt[B]=a1[B] Ù
alt[C]=a2[C] Ù alt[T]=a1[T]a2[T] }
Alessio Bottrighi 's Ph.d. Dissertation

35. Cooperative Update: algebra(3)
Definition: convert
convert(proposal(r))={(a1,..,a1,u, a’1,…,a’n,u',T)\ ptproposal(r) \(a1,…,an,u)=origin(pt) (a’1,…,a’n,u’)=alternatives(pt) }
Alessio Bottrighi 's Ph.d. Dissertation

36. Alessio Bottrighi 's Ph.d. Dissertation
End
Thanks!!
Alessio Bottrighi 's Ph.d. Dissertation