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Advanced treatment of temporal phenomena in clinical guidelines

Advanced treatment of temporal phenomena in clinical guidelines. Paolo Terenziani 1 , Luca Anselma 2 , Alessio Bottrighi 1 , Stefania Montani 1 1 DI, Univ. Piemonte Orientale “A. Avogadro”, Via Bellini 25g, Alessandria, Italy 2 DI, Università di Torino, Corso Svizzera 184, 10149 Torino, Italy.

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Advanced treatment of temporal phenomena in clinical guidelines

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  1. 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

  2. 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,…)

  3. 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

  4. GLARE Representation Formalism

  5. Representation FormalismHierarchy of Action Types Action Plan Work action Query Decision Conclusion Clinical action Pharmacol. prescription Diagnostic decision Therapeutic decision

  6. Representation Formalismdescription of a clinical action

  7. Clinical DB Expert Physician User Physician Pharmac. DB Acquisition Interface Execution Interface Resource DB Knowledge Manager Execution Module ICD DB Guidelines DB Guidelines Instantiation DB Patient DB Architecture of the system

  8. 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

  9. Temporal Constraints in Clinical Guidelinesrepetitions 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.

  10. 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

  11. A B C 10-20 10-20 10-20 Managing Temporal Constraints: the Problem 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!

  12. 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)

  13. A B C 10-20 10-20 10-20 Digression: Why Completeness is fundamental? 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!

  14. 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

  15. 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

  16. 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.

  17. 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).

  18. Temporal reasoning on guidelines + instantiationsALGORITHM (sketch)

  19. Temporal reasoning on guidelines + instantiations

  20. 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

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