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Kahn's TOPAZ System (1988)

Temporal Reasoning and Planning in Medicine Temporal Reasoning In Medical Information Systems (II) Yuval Shahar M.D., Ph.D. Kahn's TOPAZ System (1988). • Input: point-based , unambiguous time-stamped clinical data • An integrated , multiple-temporal–model interpretation scheme

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Kahn's TOPAZ System (1988)

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  1. Temporal Reasoning and Planning in MedicineTemporal ReasoningIn Medical Information Systems (II)Yuval Shahar M.D., Ph.D.

  2. Kahn's TOPAZ System (1988) • Input: point-based, unambiguous time-stamped clinical data • An integrated, multiple-temporal–model interpretation scheme • A numeric model that models quantitatively underlying processes and modifies an atemporal, population-based model into a temporal, patient-specific model • A symbolic, interval-based model that aggregates clinically interesting events into an interval-based hierarchy using context-specific rules • A symbolicstate-based model that generates text paragraphs in the domain's language from the interval-based abstractions, using ATNs • The TNET management system and the temporal-query language TQuery • TNET was extended to ETNET, a knowledge-representation, temporal-reasoning and temporal-management system that used context-specific rules

  3. Summarization Steps in TOPAZ

  4. The TOPAZ System: An Analysis • Highly domain-specific model (one parameter, anatomic site. Disease, drug); not clear how reusable it would be across other clinical domains • Many domains defy complete quantitative numeric modeling - when should modeling stop? (another component might create instability) - which data should be considered? (historical versus recent data) - which parameter should be modified if necessary? (credit assignment) - how should over-fitting be avoided? (especially if a crucial parameter is missing) • The expected population predictions compared to patient-specific predictions (since these are smoother), not to the observed data - detection of change in patient parameters is more difficult - generation of explanations is more challenging, since users look at observed data

  5. Larizza’s M-HTP System(1992) • Monitors heart-transplant patients • Uses a temporal net similar to Kahn's TNET, and a relational database management system • An object-oriented visit hierarchy of visits, off which patient-specific parameters and their values are indexed • An object-oriented knowledge base of significant episodes -Parameter objects include constructs such as Hemoglobin-decrease • A temporal-pattern-matching language used for antecedents of rules: "An episode of PLT-count-decrease overlaps an episode of WBC-count-decrease for at least 3 days during last week"

  6. The Patient-Visits Hierarchy in M-HTP

  7. The Significant-Episodes Taxonomy in M-HTP

  8. Significant Episodes in M-HTP REPRESENTATION OF SIGNIFICANT EPISODES 25 Nov 90 05 Dec 90 15 Dec 90 25 Dec 90 04 Jan 91 Negative_CMV_viremia + + 56 45 25 42 59 24 CMV_antigenemia_increase 24 59 CMV_viremia_increase + + 31 28 37 56 WBC_decrease 59 21

  9. The M-HTP System: An Analysis • A domain-specific instance of more general architectures • The knowledge base encodes hard-coded instances such as WBC-decrease as opposed to instances of the general class gradients for the decreasing value - does not enable inheritance of default values from the gradient class - does not enable sharing of properties among all hematological gradients • No separation of domain-independent knowledge from domain-specific properties - abstractions are not “first class citizens” in the knowledge base, and cannot be described and manipulated using the full language

  10. Kohane’s TUP system(1986) • A temporal utilities package (TUP) system • Demonstrated by the temporal hypothesis reasoning in patient history taking (THRIPHT) medical expert system • A point-based, flexible representation based on the range relation (RREL) creates a constraint network using a restricted, computationally tractable algebra (in particular, no disjunctions such as A <before|after> B) (RREL <point 1 specification> <point 2 specification> <lower bound> <upper bound> <context>) e.g., (RREL ((event MI) (type BEGIN-INTERVAL) (event SGOT-PEAK) (type BEGIN-INTERVAL) (24 hrs) (48 hrs)) • Can reason about alternate temporal hypotheses (monitor contexts)

  11. The TrenDx System(Haimovitz and Kohane, 1993) • Built on top of Kohane's constraint-network TUP system • Encodes patterns as trend templates (TTs) that describe typical clinical patterns as a set of vertical and horizontal constraints • a TT has a set of value constraints of the form minf(D) MAX; min, MAX, are minima and maxima of the function f defined over the measureable parameters D in the temporal range of the interval. • TTs can be matched to partial patterns by maintaining an agenda of candidate patterns that might fit the data (even one point) - A goal-directed approach to pattern matching, starting with pattern • Tested on cases in the growth-chart and intensive-care domains

  12. A Normal-Growth TT in TrenDx

  13. The TrenDx System: An Analysis • Different goals from other systems: Matching of top-level temporal patterns to raw data rather than abstraction or summarization • No knowledge base or abstraction hierarchy such as in IDEFIX, etc. - TTs need to be re-constructed for new tasks: parts are not reused - no knowledge roles (e.g., significant deviation) that can be reused - does not enable use of inheritance among pattern types and instances - no capability for answering queries regarding intermediate-level abstractions - no capability for using intermediate abstractions in the input, since pattern matching must start from raw data • Acquisition of new TT involves definition of all levels of abstraction at the same time - no intent to facilitate elicitation from users • Like other systems, assumes incomplete information about the clinical domain which prevents construction of complete numerical models - associative patterns when knowledge is incomplete and data is sparse

  14. Summary • Temporal representations in medical information systems have moved from symbolic tokens and strings (e.g., MYCIN and Internist-I) to syntactic-level temporal control systems (e.g., TCS), to explicit knowledge-based systems (e.g., VM, IDEFIX), and finally to semantic-level temporal abstraction systems (e.g., TrenDx, M-HTP) • A major issue that is at the focus of current research is facilitation of acquisition, maintenance, reuse and sharing of domain-specific (medical) knowledge

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