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Temporal Databases and Maintenance of Time-Oriented Clinical Data

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Title: Temporal Databases and Maintenance of Time-Oriented Clinical Data


1
Temporal Databases andMaintenance 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 --gt 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)
  • gt 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
    patients current address and phone number)
  • Historical databases Have only valid time (e.g.,
    a series of updates of the patients 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)

Body
Beginning
End
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 ltFunction Attribute-Name Whengt
  • (that is, perform Function on Attribute-Name
    during When)
  • When ltInterval-Name Range ltWhengt Pname
    Pconditiongt (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 gt 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
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
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 400 MINUTE)
  • VALID PERIOD 2000-07-23 2000-8-14

20
Chronus II(OConnor 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 events
    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) gt2

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
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