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Advanced Temporal Data Abstraction for Guideline Execution

Advanced Temporal Data Abstraction for Guideline Execution. Andreas Seyfang and Silvia Miksch Vienna University of Technology Austria. Overview. Motivation Why Guideline Execution Why Temporal Data Abstraction Definitions Abstractions with example Strength & limitations.

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Advanced Temporal Data Abstraction for Guideline Execution

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  1. Advanced Temporal Data Abstraction for Guideline Execution Andreas Seyfang and Silvia Miksch Vienna University of Technology Austria

  2. Overview • Motivation • Why Guideline Execution • Why Temporal Data Abstraction • Definitions • Abstractions with example • Strength & limitations

  3. Why Guideline Execution? • Deliver the right recommendation at the right time • Reduce information overload • Improve quality of health care • Prerequisites • Information about patient state • Formal representation of guideline

  4. Why Data Abstraction? • Integration into clinical data flow necessary Additional data entry = additional work = barrier to usage of guideline execution system

  5. Why Data Abstraction? • Integration into clinical data flow necessary • Gap between raw data and medical concepts • Quantitative raw data: • 11:23:05 SpO2=96% • 11:23:06 SpO2=95% • 11:23:07 SpO2=96% • Qualitative medical concept: sufficient oxygen supply in artificial ventilation

  6. Why Temporal Data Abstraction? • Temporal dimension crucial part of medical concepts (often implicit) • Recent readings of SpO2 • Combinations of different time windows necessary • Short term trend can invalidatelong term observation

  7. Plan Library InstantiatedPlans Patient Data * Raw Data * * + + Temporal DataAbstraction Guideline Execution Qualitative High-level Information Context INPUT Time-Oriented The Big Picture

  8. Definitions • Parameter = Variable plus history of measurements • Episode = period of time during which a parameter has a certain value • Parameter proposition = parameter + value constraint + context + temporal constraints

  9. Simple Example • Solution • Parameter proposition • Parameter name: SpO2 • Value constraint: less than 80 • Context: artificial ventilation • Minimum duration: 4 seconds In artificial ventilation, hypoxic episode is a period of time lasting longer than 4 seconds during which the SpO2 reading is below 80.

  10. Abstractions • Qualitative values • Sliding time windows • Statistical measures • Time/date oriented abstraction • Repetitions

  11. Combinations/Temporal Patterns • Logical • and, or • Arithmetic • sum, difference • Different parameter propositions • Aggregates of different time ranges

  12. Complex Example • Solution • Hypoxy as before • Hyperoxy similar but time constraint:Latest start 20 seconds after end of hypoxy Overshooting hyperoxy is an episode of dangerously increased SpO2 which starts at less than 20 seconds after a hypoxic episode. SpO2 > 96 considered dangerous.

  13. Raw Data Input SpO2 Parameter Proposition Hypoxy Parameter Change Hypoxy-End Parameter Proposition Dangerous Hyperoxy Interactive Configuration

  14. Strengths • Detection of episodes • Aggregates for sliding time windows • Monitoring of repetitions • Free combination of abstractions

  15. Limitations • Knowledge acquisition • Access to all required inputs • Integration with precise formalized guideline • Some abstractions not implemented

  16. Conclusion • Guideline execution needs temporal data abstraction • Temporal data abstraction needs guideline execution

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