Applying AI temporal reasoning techniques to Clinical Guidelines

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Applying AI temporal reasoning techniques to
Clinical Guidelines
Luca Anselma, Paolo Terenziani, Stefania
Montani, Alessio Bottrighi DI, Università di
Torino, Corso Svizzera 185, 10149 Torino,
Italy Phone [phone] E-mail
anselma_at_di.unito.it DI, Univ. del Piemonte
Orientale Amedeo Avogadro Spalto Marengo 33,
15100 Alessandria, Italy Phone [phone]
E-mail terenz,stefania,alessio.bottrighi_at_mfn.un
ipmn.it
- Introduction
- Temporal Constraints in Clinical Guidelines
new issues
- An extension to AI Temporal Reasoning Techniques
- Conclusions
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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,)
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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) - User-friendl
y (limited number of primitives)
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GLARE Representation Formalism
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Temporal Constraints in Clinical Guidelines
Temporal constraints are an intrinsic part of
clinical knowledge (e.g., ordering of the
therapeutic actions)
(1) standard AI constraints - duration of
actions (min / max) - qualitative constraints
(e.g., before, during) - delays (min /
max) Explicit (duration of actions, delays
between actions) or induced from the control
relations (ordering of actions)
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Temporal Constraints in Clinical Guidelines
(2) Implicit constraints induced from the part-of
relations - standard containment constraints
(!?)
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Temporal Constraints in Clinical Guidelines

• (3) Explicit constraints on repeated actions
• (Ex. 2) Intrathecal methotrexate must be
  administered 7 times during 88 weeks, never less
  than 10 weeks apart or more then 14 weeks apart.
• NOTICE the number of repetitions may be unknown
• (Ex. 3) Give acetaminophen twice a day until the
  fever has gone.
• NOTICE complex interplay with part-of (nested
  repetitions)
• (Ex. 1) The 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.

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Temporal Constraints in Clinical Guidelines

• (4) Distinction between
• constraints between classes of actions
• constraints between instances of actions
• Inheritance of constraints
• Predictive role of classes

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Temporal Constraints in Clinical Guidelines
OUR CHALLENGE REPRESENTING AND REASONING WITH
ALL SUCH CONSTRAINTS IN AN INTEGRATED WAY
NO OTHER APPROACH IN THE LITERATURE
CHALLENGING EXTENSION TO STANDARD APPROACHES
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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!
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Our solution two layered approach

• HIGH-LEVEL Language
• part-of
• Instance of
• Repetition

Repetition(A, R1, R2, , Rn), Ri
ltnRepetitionsi, I-Timei, repConstraintsi,
conditionsigt, repConstraintsi ?
fromStart(min, max), toEnd(min, max),
inBetweenAll(min, max) inBetween((min1,
max1), , (minnRep-1, maxnRep-1)), Conditionsi ?
onlyIf(B), while(B)
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Our solution two layered approach

• LOW-LEVEL language Temporal Reasoning

Based on STP Decther et al., 91
Extensions NEEDED!
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Starting Point STP framework
Conjunctions of b.o.d. constraints c?X-Y ?d
Can be used to represent distances between points
(starting/ending points of actions)
Floyd-Warshall all-to-all shortest paths algoritm
is correct and complete for the STP framework,
and operates in O(N3) (where N is the number of
variables - time points) It produces the minimal
network of the constraints (i.e., the shortest
path between each pair of nodes)
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Labeled tree of STPs (STPs-tree)






168d,168d

























N1
Sch
Ech




















ltlt6, 24wk, inBetweenAll(23d, 23d),
toEnd(23d,23d), Æ gtgt







0d,0d




0d,0d































N2
Spc





















Smc































5d,5d
5d,5d
5d,5d
5d,5d
















































Emc
Epc













































0d,0d








0d,0d
ltlt5, 5d, Æ, Ægt,lt2,2d, Æ , Æ gtgt
ltlt5, 5d, Æ, Ægt,lt1,1d, Æ , Æ gtgt








,
,


,


































0d,1d
0d,1d





























































N3




N4
Ep
Sp









Sm








Em




STP-tree for the multiple mieloma chemotherapy
guideline in Ex. 1. Thin lines and arcs between
nodes in a STP represent bound on differences
constraints. Arcs from a pair of nodes to a child
STP represent repetitions. Sch, Ech, Smc, Emc,
Spc, Epc, Sm, Em, Sp and Ep stand for the
starting (S) and ending (E) points of
chemotherapy, melphalan cycle, prednisone cycle,
melphalan treatment and prednisone treatment,
respectively.


.
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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 function
STP_tree_consistency(X STPNode, RepSpec)) STP
(1) check that the repetition/periodicity
constraint is well-formed (i.e., that repetitions
nest properly) (2) compute Max, i.e. the maximum
duration of a single repetition of X according to
RepSpec (3) impose in X that the maximum
distance between each pair of points is less or
equals Max (4) X ? FloydWarshall(X) (5) if X
INCONSISTENT then return INCONSISTENT else return
X
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Consistency checking on STPs-trees
ALGO1 Properties Complexity. Considering that
the number of nesting levels, in the worst case,
is less than the number of classes, the algorithm
is dominated by step 4, that is O(C3), where C is
the number of actions in the guideline. Property
1. The top-down visit of the STP-tree is complete
as regards consistency checking of the
constraints in the STP-tree.
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Temporal reasoning algorithms on
STPs-treesclassesinstances
ALGO 2
function integratedConsistency(T STP-tree, E
executionSTP, NOW) STP a. (1) check that in the
executionSTP there are all and only the instances
that the STP-tree predicts to be. (2) Possible
missing instances are hypothesized because they
may happen in the future // this step deals
with the predictive role of the temporal
constraints about classes b. (3) inherit the
repetition/periodicity constraints and the
temporal (non-periodic) constraints from the
classes to the instances //this step copies
all temporal constraints to the executionSTP c.
(4) propagate the temporal constraints on the
executionSTP, thus obtaining the minimal
network (5) check whether the hypothesized
instances expected in the future may actually
start in the future (i.e., after NOW)
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Temporal reasoning algorithms on
STPs-treesProperties
Complexity. Let us denote with C the number of
classes in the STP-tree and with I the number of
instances in the executionSTP. The complexity of
integratedConsistency procedure is O(maxC3,
I3).   Property 2. The integratedConsistency
procedure is correct and complete as regards
consistency checking of the constraints in the
executionSTP and in the STP-tree.
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Temporal Reasoning for Clinical Guidelines

• During acquisition, to check consistency (Algo 1)
• During execution
• (1) for scheduling the next action
• (2) for quality evaluation check whether
  classes constraints have been respected by
  instances (Algo 2)
• (3) to support decision making queries
• (4) to support decision making comparing
  guidelines paths

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Temporal Reasoning for Clinical Guidelines

• e.g., for task 4
• For each path Pi to be compared
• 2. Hypothesize the existence of an instance of
  each action in Pi which has not been executed yet
• 3. Apply the algorithm in Fig. 3 to the
  (executed and hypothesized) actions in Pi, to
  determine the minimal network MNi
• Retrieve the minimal and maximal duration of Pi
  from MNi

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Conclusions

• Temporal constraints are an intrinsic part of
  clinical guidelines
• Implicit explicit constraints
• NEW CHALLENGING PROBLEMS
• OUR SOLUTION
• - analysis of the trade-off between
  expressiveness and complexity
• - definition of STPs-trees
• - tractable, correct and complete constraint
  propagation algorithms
• - additional algorithms, to exploit constraint
  propagation in the guideline context
• CONCLUSION TR can give relevant contributions
  to the MI field!