1. Temporal Databases and Maintenance of Time-Oriented Clinical Data Yuval Shahar, M.D., Ph.D.

2. A Clinical Scenario Ms. Jones was seen in the diabetes clinic on January 14 1997 at 11 A.M. Her blood-glucose value at that time was measured in the clinic as 220 mg/100ml. She complained of vomiting and dizziness for the past 2 or 3 days. She was eventually hospitalized on the same day. A more accurate blood- glucose test that was taken at the same time as the one performed in the clinic returned from the laboratory on January 15 1997, with a value of 380 mg/100ml. Ms. Jones was discharged on January 17 1997, and was seen again in the clinic on January 24 1997. At that time, several renal-function serum and urine tests were performed in addition to measuring blood-glucose values. A complete neurological assessment was carried out as well.

3. Uses of Clinical Data Clinical decision making –monitoring –diagnosis –therapy Clinical research Administration and other tasks –Quality assessment –Billing Legal records

4. Clinical Database Features Clinical data is time oriented –different temporal aspects, such as when was the data valid, versus when was the data recorded Often, there is inherent uncertainty regarding the time, value, or both aspects of the data Data are often incorrect, incomplete, or inconsistent Might require a specialized database management system (DBMS)

5. Data Quality Issues Correctness –Validation during data entry –Validation by global data analysis Completeness –Missing observations Possible bias due to hidden contexts Possible completion from neighboring values Possible completion from related data types Consistency –Consistent semantics over patients and time

6. A Temporal Query “Determine if the oncology patient (currently under therapy by a chemotherapy protocol) had within the past 6 months at least two episodes that lasted for more than 3 weeks, of Grade II bone-marrow toxicity (due to a specific chemotherapy drug)” Responding to such queries is necessary to support clinical management, such as when using a clinical guideline

7. The Time-Oriented Database (TOD) Developed at Stanford during the 1970s A cubic, three-dimensional structure –patients X visits X clinical parameters --> value Microcomputer version: MEDLOG Two file structures: –One indexed by patients, for individual information –One indexed by parameter type, for statistical analysis

8. The ARAMIS Database The American Rheumatism Association Medical Information System (ARAMIS) Developed at Stanford during the 1970s and maintained since that time in multiple sites Contains longitudinal data concerning multiple patients who have rheumatic diseases or arthritis Originally used TOD, then MEDLOG and other tools for analysis of chronic diseases

9. Types of Temporal Dimensions (Snodgrass and Ahn, 1986) Transaction time: The time in which (or during which) data are stored in the database (e.g., in which “the patient has mild anemia” was recorded) Valid time: The time during which the data were true (e.g., the period during which the patient did, in fact, have mild anemia) User-defined time: A time stamp or interval that is specific to the application (e.g., the time in which the anemia level was determined in the laboratory) => Transaction time and valid time define the database type

10. Database Types, A Temporal View Snapshot databases: Have no time aspect (flat records) Rollback databases: Have only transaction time (e.g., a series of time-stamped updates to the patient’s current address and phone number) Historical databases: Have only valid time (e.g., a series of updates of the patient’s state of anemia during January 1997, deleting previous values that refer to that time period, keeping only the latest updates) Bitemporal databases: Have both transaction time and valid time (e.g., on February 12 1997, it was recorded that, during January 1997, the patient had mild anemia)

11. A Tale of Two Data Types

12. Time and Uncertainty There is often uncertainty as to when the clinical episode started or ended, and what its duration was One way of representing such uncertainty is by using a Variable Time Interval (sometimes augmented by min/max duration constraints) BodyBeginningEnd Time

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

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

15. The TQuery Language (Kahn, 1991) Used within the TNET temporal network system, which was used by the Stanford ONCOCIN oncology-therapy automated protocol-based system during the 1980s Each TNODE represents a time interval during which a clinical event happened TQuery allows users to store and retrieve data using clinical contexts rather than dates Query::= –(that is, perform Function on Attribute-Name during When) When::= Pname Pcondition> (a recursive temporal specification)

16. Tquery Examples (Visit (1 4)) –The first to fourth TNODES with type label = Visit (Visit FIRST (Cycle (-4 –1))) –The first of each of the TNODES with label = Visit from the last four TNODES with type label = Cycle ((Visit Tx) All ((CmTx POCC) ALL) WBC (NCOMPARE > $ 4.5) -Select from all (type chemotherapy, subtype POCC) all the nodes with (type visit, subtype Tx) in which the value of attribute WBC exists and is greater than 4.5

17. TSQL2 (Snodgrass, 1995) Designed by a committee of researchers, headed by Snodgrass at Arizona University Consolidates existing approaches Inherits from SQL-92 temporal types such as DATE –Adds the PERIOD data type A linear, bounded at both ends, time line No commitment to discrete, dense, or continuous temporal ontologies: Queries must include granularity to be meaningful A bitemporal conceptual data model, timestamps tuples by a set of bitemporal chronons; each chronon (t, v) is a rectangle in valid time/transaction time space

18. The Bitemporal Conceptual Data Model Transaction Time Valid Time 23/2/951/4/9521/6/953/7/95 23/2//95 17/3/95 5/1/95 27/11/94 Hospitalized(Jane)

19. TSQL2: Examples What drugs were prescribed to Jane in 1996? SELECT Drug VALID INTERSECT (VALID (Prescription), PERIOD ‘[1996]’ DAY) FROM Prescription WHERE Name = ‘Jane’ Insert a prescription with a known period of validity INSERT INTO Prescription VALUES (‘Jane’, ‘Dr. Max’, ‘Lasix’, ’50mg’, INTERVAL ‘4:00’ MINUTE) VALID PERIOD ‘[2000-07-23 – 2000-8-14]’

20. Chronus II (O’Connor et al., 1999) A Stanford model, influenced by TSQL2 and the previous Das Chronus system, which it considerably enhances Designed to support queries in the EON guideline- based therapy system and the Tzolkin temporal mediator to patient data Supports most of SQL-92 as well as extensions such as valid time, indeterminacy, multiple calendars, hierarchical types, temporal joins, etc. Temporal indeterminacy uses the Snodgrass model of lower support, upper support, and a probability mass function to denote the event’s temporal distribution

21. Chronus II: Example Select employees that have worked as a mechanic for longer than two months: TEMPORAL SELECT Name FROM Occupation WHERE Title = ‘Mechanic’ WHEN DURATION(Occupation, ‘Months’) >2

22. Summary Clinical databases require representations that include a strong emphasis on time and uncertainty Bitemporal databases are necessary to support clinical, research, administrative and legal requirements