Temporal maintenance and temporal databases Temporal Reasoning rheumatism

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Temporal maintenance and temporal databases Temporal Reasoning  rheumatism Powered By Docstoc
					  Temporal Databases and
     Maintenance of
Time-Oriented Clinical Data

  Yuval Shahar, M.D., Ph.D.
                   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

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.
       Uses of Clinical Data
• Clinical decision making
  – monitoring
  – diagnosis
  – therapy
• Clinical research
• Administration and other tasks
  – Quality assessment
  – Billing
• Legal records
    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
• Might require a specialized database
  management system (DBMS)
             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
          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
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
      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
  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
• 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
 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)
             A Tale of Two Data Types

Parameter name           Value/unit          Valid              Valid              Transaction
(event name)             (attribute:value)   start time         stop time           time
Blood-glucose level      120 gr/100cc        1/17/96:9:00a.m    1/17/96:9:00a.m    1/17/96:10:00a.m.
Insulin administration   dose:2 units        1/17/96:8:00a.m.   1/17/96:8:02a.m.   1/17/96:10:05a.m.
Blood-glucose level      130 gr/100cc        1/18/96:9:00a.m.   1/18/96:9:00a.m.   1/18/96:6:00p.m.
Insulin administration   dose:3 units        1/18/96:8:00a.m.   1/18/96:8:01a.m.   1/18/96:7:00p.m.
Blood-glucose level      190 gr/100cc        1/18/96:9:00a.m.   1/18/96:9:00a.m.   1/20/96:8:00a.m.
          Time and Uncertainty
• There is often uncertainty as to when the clinical
  episode started or ended, and what its duration
• One way of representing such uncertainty is by
  using a Variable Time Interval (sometimes
  augmented by min/max duration constraints)
     Beginning        Body       End

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

DB           TM             decision-support      TR
      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)
             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::= <Function Attribute-Name When>
    – (that is, perform Function on Attribute-Name during When)
• When::= <Interval-Name Range <When> Pname
  Pcondition> (a recursive temporal specification)
                  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
                      (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
• 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
     The Bitemporal Conceptual
             Data Model

             23/2/95   1/4/95             21/6/95 3/7/95

                          Transaction Time
              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]‟
                  Chronus II
               (O‟Connor et al., 1999)
• A Stanford model, influenced by TSQL2 and the
  previous Das Chronus system, which it considerably
• 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
         Chronus II: Example
• Select employees that have worked as a
  mechanic for longer than two months:
    FROM Occupation
    WHERE Title = „Mechanic‟
    WHEN DURATION(Occupation, „Months‟) >2
• Clinical databases require representations that
  include a strong emphasis on time and
• Bitemporal databases are necessary to support
  clinical, research, administrative and legal