1. Temporal Mediators: Integration of Temporal Reasoning and Temporal-Data Maintenance Yuval Shahar MD, PhD Temporal Reasoning and Planning in Medicine

2. Temporal Reasoning and Temporal Maintenance n Temporal reasoning supports inference tasks involving time-oriented data; often connected with artificial-intelligence methods n Temporal data maintenance deals with storage and retrieval of data that has multiple temporal dimensions; often connected with database systems n Both require temporal data modelling

3. Examples of Temporal- Maintenance Systems n TSQL2, a bitemporal-database query language (Snodgrass et al., Arizona) n TNET and the TQuery language (Kahn, Stanford/UCSF) n The Chronus/Chronus2 projects (Stanford)

4. Examples of Temporal-Reasoning Systems n RÉSUMÉ n M-HTP n TOPAZ n TrenDx

5. A Typical TM and TR Application: Automated Support to Therapy by Clinical Guidelines/Protocols  Clinical guidelines/protocols contain recommendations for medical interventions that are predicated on the observation of: u relevant temporal patterns of these states u relevant patient states

6. CCTG-522 Recommendation Modify the standard dose of AZT for a patient if, during treatment with the protocol, the patient experiences a second episode of moderate anemia that has persisted for more than two weeks Modify the standard dose of AZT for a patient if, during treatment with the protocol, the patient experiences a second episode of moderate anemia that has persisted for more than two weeks

7. Protocol-Based Decision Support System n Presents patient-specific recommendations n Needs a method for verifying the presence of time-oriented patient conditions in a database

8. Information Mismatch

9. Temporal Abstraction n Defined as the creation of high-level summaries of time-oriented data n Necessary because u clinical databases usually store raw, time-stamped data u protocols often require information in high-level terms

10. Temporal Patterns

11. Temporal Maintenance n Defined as the storage of time-oriented data and the selective retrieval of that data based on some time-oriented constraint n Necessary because clinical conditions may be defined as temporal patterns u temporal order u temporal duration u temporal context

12. Temporal Data Manager n Performs u temporal abstraction of time-oriented data u temporal maintenance n Used for tasks such as finding in a patient database which patients fulfil eligibility conditions (expressed as temporal patterns), assessing the quality of care by comparison to predefined time- oriented goals, or visualization temporal patterns in the patient data

13. Embedding A Temporal Data Manager Within a Guideline- Support System n Can be embedded within a larger decision support framework, e.g., EON n Mediates all access to the external clinical database

14. 1) Extend DBMS 2) Extend Application Two Implementation Strategies

15. Problems Extending DBMS Temporal data management methods implemented in DBMS: u are limited to producing very simple abstractions u are often database- specific

16. Problems Extending Applications Temporal data management methods implemented in applications: Temporal data management methods implemented in applications: u duplicate some functions of the DBMS u are application-specific

17. Our Strategy n Separates data management methods from the application and the database n Decomposes temporal data management into two general tasks: u temporal abstraction u temporal maintenance

18. The Tzolkin Temporal Mediator Architecture

19. RÉSUMÉ: Temporal Abstraction n Creates summaries of time-oriented data u Clinical data is usually stored as “low-level” data u Protocols often specify conditions as “high-level”, interval-based concepts n Is domain-independent n Has a tool that facilitates knowledge acquisition and maintenance

20. Temporal Abstraction of Hb

21. Chronus: Temporal Maintenance n Provides temporal extensions to SQL n Historical relational model u Each tuple has two time stamps u Time stamps conferred special status n Temporal algebra that supports temporal manipulations u Closed algebra u Complete for the temporal conditions found in protocols

22. Chronus TimeLine SQL (TL-SQL) GRAINWEEK SELECT2ND problem_name FROMproblems_table WHEREproblem_name = ‘Hb’ WHENSTART_TIME BEFORE 1/1/99

23. RÉSUMÉ and Chronus Coupling RÉSUMÉ and Chronus n Integrates temporal abstraction and temporal query processing n Allows retrieval of summaries of clinical data using time-oriented conditions Modify the standard dose of AZT for a patient if, during treatment with the protocol, the patient experiences a second episode of moderate anemia that has persisted for more than two weeks Modify the standard dose of AZT for a patient if, during treatment with the protocol, the patient experiences a second episode of moderate anemia that has persisted for more than two weeks

24. SQLA Interface Language n Based on SQL n Supports temporal queries n Detects when abstractions are requested and computes them on the fly GRAINWEEK CONTEXTCCTG-522 CONTEXTCCTG-522 SELECT2ND problem_name FROMproblems_table WHEREproblem_name = ‘HbState’ and value = ‘moderate anemia’ WHENDURATION (start, stop) > 2

25. Query-Evaluation Algorithm

26. A Detailed Example GRAINWEEK CONTEXTCCTG-522 CONTEXTCCTG-522 SELECT2ND problem_name FROMproblems_table WHEREproblem_name = ‘HbState’ and value = ‘moderate anemia’ WHENDURATION (start, stop) > 2

27. Loading the Domain Knowledge n Examine the context clause of the SQLA statement, which contains a reference to a knowledge base n Use the reference to locate and load the appropriate knowledge base

28. Detecting the Need for Abstractions n Find non- SQLA terms in WHERE clause (“HbState” and “moderate anemia”) n Look up terms in RESUME KB n If look-up succeeds, Tzolkin needs to compute abstractions (“HbState”) HbWBCPlt HbStateWBCStatePltState Blood State

29. Loading the Data Primitives n Locate the requested abstraction in the RESUME KB (“HbState”) n Find the primitive parameters (leaves of the tree) below it (“Hb”) n Load all patient data of these parameter types into RESUME HbWBCPlt HbStateWBCStatePltState Blood State

30. RÉSUMÉ Generating the Interpretation Contexts within RÉSUMÉ n Find the types of events and abstractions that can induce a context (via a dynamic induction relation of contexts) (context CCTG522 can be induced by event: “enroll-CCTG 522”) n Locate patient-specific instances of these events (patient enrolled in CCTG 522 on 10/10/1999) n Compute all abstractions that can induce a context (recursive process) n RESUME will then generate the appropriate contexts

31. RÉSUMÉ Invoking RÉSUMÉ and Chronus n Execute RESUME to compute the requested abstractions u The computed abstractions are stored in the database u RESUME signals Tzolkin when it is done n Then execute Chronus to retrieve the results

32. Future Research Directions n Enhancement of the query language n Addition of truth-maintenance capabilities to the database n Addition of “what-if” query support n Provision of complete dynamic (goal-directed) query computation