Advanced treatment of temporal phenomena in
clinical guidelines
Paolo Terenziani1, Luca Anselma2, Alessio Bottrighi1, Stefania Montani1
1DI,
Univ. Piemonte Orientale “A. Avogadro”, Via Bellini 25\g, Alessandria, Italy
2DI, Università di Torino, Corso Svizzera 184, 10149 Torino, Italy
- Introduction
- GLARE (GuideLine Acquisition, Representation and Execution)
- Temporal constraints and Temporal Reasoning: generalities
- The GLARE approach (data structures, algorithms and properties)
- Conclusions
Introduction
Clinical guidelines are a means for specifying the “best” clinical
procedures and for standardizing them
Adopting (computer-based) clinical guidelines is advantageous
Different roles:
support
critique
evaluation
education
…...
Many different computer systems managing clinical guidelines
(e.g., Asgaard, GEM, Gliff, Guide, PROforma,…)
GLARE
(GuideLine Acquisition Representation and
Execution)
Joint project:
Dept. Comp. Sci., Univ. Alessandria (It): P. Terenziani, S.Montani, A.Bottrighi
Dept. Comp. Sci., Univ. Torino (It): L.Anselma,G.Correndo
Az. Osp. S. Giovanni Battista, Torino (It): G.Molino, M.Torchio
-
Domain independent
(e.g., bladder cancer, reflux esophagitis, heart failure)
Phisician-oriented & User-friendly
Some recent pubblications:
Terenziani et al., AIIMJ 01,07a,07b, AMIA 00,02,03, Medinfo 04,
CGP’04a,04b, AI*IA 03,05, GIN 04,05, AIME 05a,05b,05c
AMIA’06 posters M010 and T120
AMIA’06 paper in session S52
GLARE
Representation Formalism
Representation Formalism
Hierarchy of Action Types
Action
Plan
Work action
Clinical
action
Query
Pharmacol.
prescription
Decision
Diagnostic
decision
Conclusion
Therapeutic
decision
Representation Formalism
description of a clinical action
basic
description
name
description (text)
preconditions
logical
contextual
must include
may include
must exclude
may exclude
conflicts
cost
time
resources
goals (text)
frame time
action-time
repetitions
delay time
repet-time
execution time
frequency
Exit-condition
Architecture of the system
Clinical DB
Expert
Physician
User
Physician
Acquisition
Interface
Execution
Interface
Knowledge
Manager
Execution
Module
Pharmac. DB
Resource DB
ICD DB
Guidelines
DB
Guidelines
Instantiation DB
Patient
DB
Temporal Constraints in Clinical Guidelines
Temporal constraints are an intrinsic part of clinical knowledge
(e.g., ordering of the therapeutic actions)
Different kinds of temporal constraints, e.g.,
-
duration of actions (min / max)
-
qualitative constraints (e.g., before, during)
-
delays (min / max)
-
periodicity constraints on repeated actions
Temporal Constraints in Clinical Guidelines
repetitions
TheThe therapy for multiple mieloma is made by six cycles
of 5-day treatment, each one followed by a delay of 23 days
(for a total time of 24 weeks). Within each cycle of 5 days,
2 inner cycles can be distinguished: the melphalan
treatment, to be provided twice a day, for each of the 5
days, and the prednisone treatment, to be provided once a
day, for each of the 5 days. These two treatments must be
performed in parallel.
Managing Temporal Constraint: the Problem
Both representation and inference are NEEDED
Temporal Constraints without Temporal
Reasoning (constraint propagation)
are useless
clash against users’ intuitions/expectations
Managing Temporal Constraints: the Problem
(1.1) the end of A is equal to the start of B
(1.2) the end of B is equal to the start of C
(1.3) the duration of A is between 10 and 20 m
(1.4) the duration of B is between 10 and 20 m
(1.5) the duration of C is between 10 and 20 m
A
10-20
B
10-20
C
10-20
Implied constraint (temporal reasoning):
(1.6) C ends between 30 and 60 m after the start of A
Correct (consistent) assertion:
(1.7) C ends between 30 and 50 m after the start of A
Not correct (inconsistent) assertion:
(1.8) C ends more than 70 m. after the start of A
However: Temporal Reasoning is NEEDED in order
to support such an intended semantics!
Managing Temporal Constraints: the Problem
DESIDERATA for the Representation formalism
- expressiveness  capture most temporal constraints in GL
DESIDERATA for Temporal Reasoning Algorithms
- tractability  “reasonable” response time
- correctness  no wrong inferences
- completeness  reliable answers
TRADE-OFF!
SPECIALIZED APPROACHES (since ’80 in AI literature)
Digression: Why Completeness is fundamental?
(1.1) the end of A is equal to the start of B
(1.2) the end of B is equal to the start of C
(1.3) the duration of A is between 10 and 20 m
(1.4) the duration of B is between 10 and 20 m
(1.5) the duration of C is between 10 and 20 m
A
10-20
B
10-20
C
10-20
Implied constraint (temporal reasoning):
(1.6) C ends between 30 and 60 m after the start of A
Suppose that temporal reasoning is NOT
complete, so that (1.6) is not inferred
The answer to query (Q1) might be: YES
(Q1) Is it possible that C ends more than 70 m. after the start
of A?
Complete Temporal Reasoning is NEEDED in order
to grant correct answers to queries!
Temporal Constraint Treatment
WHEN Temporal Reasoning is useful in Guidelines?
ACQUISITION
-
to check consistency
EXECUTION
-
to compare the duration of paths, in hypothetical
reasoning (simulation) facilities
-
to check that the time of execution of actions on
patients is consistent with the constraints in the
guideline
to schedule next actions
-
Temporal Representation and Temporal Reasoning for
Clinical Guidelines
• Different kinds of temporal constraints
• No current approach in the AI literature covers all of them
• Our proposal: an extension of the “consensus” STP approach [Dechter
et al., 91]
• Our goal: expressiveness + correct, complete and tractable inferences
GLARE’s approach (representation)
Labeled tree of STPs (STPs-tree)
Tree of STPs for the multiple mieloma chemotherapy guideline.
The overall therapy (node N1) is composed by 6 cycles of 5 days plus a delay of 23 days . In each cycle
(node N2), two therapies are executed in parallel: Alkeran (node N3: Sa and Ea are the starting and
ending nodes), to be repeated twice a day, and Deltacorten (node N4: Sd and Ed are the starting and
ending nodes), to be repeated once a day. Arcs between any two nodes X and Y in a STP (say N2) of the
STP-tree are labeled by a pair [n,m] representing the minimal and maximal distance between X and Y.
Consistency checking on STPs-trees
ALGO1: temporal consistency of guidelines
Top-down visit of the nodes in the STPs-tree
For each node in the STPs-tree:
1) the consistency of the constraints used to specify the
repetition taken in isolation is checked;
2) the “extra” temporal constraints regarding the repetition are
mapped onto STP constraints;
3) Floyd-Warshall’s algorithm is applied to the constraints in
the STP plus the “extra” STP constraints determined at step
2.
Property 1. ALGO1 is correct, complete, and tractable (it
operates in O(N3), where N is the number of actions in the
guideline).
Temporal reasoning on guidelines + instantiations
ALGORITHM (sketch)
ALGO2: temporal consistency on guidelines execution
(1) the existence of non-observed instances whose occurrence is
predicted by the guideline is hypothesized;
(2) all the constraints in the general guidelines are inherited by the
corresponding instances (considering both observed and
hypothesized instances). This step also involves
“non-standard” inheritance of constraints about periodicity;
(3) constraint propagation is performed on the resulting set of constraints
on instances (via Floyd-Warshall’s algorithm), to check the
consistency of the given and the inherited constraints;
(4) if constraints at step 3 are consistent, it is further checked that such
constraints do not imply that any of the “hypothesized” instances
should have started before NOW.
Temporal reasoning on guidelines + instantiations
Property 2. ALGO2 is correct, complete, and tractable.
It operates in O((N+M)3), where N is the number of actions in the guideline
and M the number of instances of actions which have been executed
(and observed).
CONCLUSIONS
Temporal constraints: an essential part of clinical guidelines
Temporal constraints: need a principled treatment:
not only representation, but also
correct, complete (and tractable) inferential mechanisms
Many approaches to time in clinical guidelines in the literature, but
properties of the inferential mechanisms (if present) quite neglected
GLARE (Guideline Acquisition, Representation and Execution): a
proposal of solution based on an extension of well-consolidated
Artificial Intelligence techniques