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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
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.
       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
  inconsistent
• 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
  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
 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
  was
• One way of representing such uncertainty is by
  using a Variable Time Interval (sometimes
  augmented by min/max duration constraints)
     Beginning        Body       End



                  Time
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


                            Clinical
DB           TM             decision-support      TR
                            application
      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
                         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
     The Bitemporal Conceptual
             Data Model
Valid
Time
  17/3/95
  23/2//95
                          Hospitalized(Jane)
  5/1/95
  27/11/94




             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
  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
         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
                  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

				
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