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Bayesian Network for MSK Triage

Bayesian Network for MSK Triage. William Marsh, EECS Corey Joseph, CSEM. Aims. Demonstrate model Describe the process Describe the relationship to evidence Current status. Progression and revisions. Progression and revisions. Development of Triage BN. Structure Relevant variables

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Bayesian Network for MSK Triage

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  1. Bayesian Network for MSK Triage William Marsh, EECS Corey Joseph, CSEM

  2. Aims • Demonstrate model • Describe the process • Describe the relationship to evidence • Current status

  3. Progression and revisions

  4. Progression and revisions

  5. Development of Triage BN • Structure • Relevant variables • States of variables • Relationships between variables • Parameters (numbers) • From data • From experts • Validation

  6. Development of Triage BN cont. • Structure • Based on information from experts • Focus group • Several consultations with clinicians • Several stages of refinement

  7. Development of Triage BN cont. • For example: • It was explained that because the symptoms suggest insidious onset of injury, then there is a lessened likelihood of a sinister pathology being present. The expert also explained that age influences probability of a sinister pathology existing (e.g. if the person is over 30 years old, then there is an elevated probability of a sinister pathology existing such as cancer). • POSSBILE NEW LINK: Sinister pathology → Age

  8. Development of Triage BN cont. • Parameters • From data if possible • From expert panel otherwise

  9. Development of Triage BN cont. • Parameters • Example data

  10. Development of Triage BN cont. Parameter development and refinement using case scenarios and expert panel Patient information

  11. Development of Triage BN cont. Parameter development and refinement using case scenarios and expert panel Uncertain classification

  12. Development of Triage BN cont. Parameter development and refinement using case scenarios and expert panel Outcome

  13. Next Steps? • Quantification • Data • … including outcome and ‘true’ diagnosis • Validation • Possible trial

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