decisions from datawilliam/presentation/technology-health-soci… · decisions from data william...
TRANSCRIPT
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DECISIONS FROM DATA William Marsh Risk and Information Management Electronic Engineering and Computer Science
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Case Study: Mangled Extremities • Academic Trauma Unit at the
RLH • Amputation vs. Salvage
• Complex • Small amount of data
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Clinical Evidence • Limits of RCT
• Rare conditions • Ethical and legal issues • Costs
• Generalisable? • Works some where vs. works every where • Background knowledge need
• `Any belief that controlled trial is the only way would mean that the pendulum had swung too far but that it had come right off the hook.’ Bradford Hill, 1965
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Scoring Systems • Low clinical acceptance
• Borderline cases
• Assumes historical decisions are perfect
• Predicts the decision • Data on what doctors did • … versus what would happen to the
patient for possible decisions
Output: Amputation!
New approach needed • Integrate evidence • Predict outcome
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Bayesian Networks
• Network of uncertain variables
• Developed from: • Expert Knowledge • Data
• Application to clinical problems • Expert systems – simulate the expert • Analyse the data – decisions based on evidence
Infection=Yes Infection=No Fever=Yes 0.90 0.15 Fever=No 0.10 0.85
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Association, Causality & Interventions • Need for causal relations
• Interventions Outcomes
• Association vs. Causation • Grey hair predicts heart disease • Colouring hair to reduce risk?
• Identifying causes • Experiment (RCT) • Domain Knowledge +
Observational Data
???
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Mangled Extremity BN
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Current Focus – Physiology BN
• Models patient physiology
• Predicts coagulopathy, and risk of death
• Importance in making limb salvage decisions
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Summary: Vision • Use causal Bayesian nets to
• Integrate evidence: data, knowledge • Support decision making: estimate result of interventions
• Represent the evidence available • Source of evidence: data, literature, expert consultation • Uncertainty
• Use • Guidelines or individuals • Applicable where RCTs are impractical • Evidence for the necessity and potential benefits of a RCT
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Acknowledgements • Trauma Academic Unit at BLH
• Lt Col Nigel Tai, FRCS, Vascular surgery • Zane Perkins, Academic Research Fellow, PhD student
• Risk and Information Management, EECS • Professor Norman Fenton, head of the research group • Professor Martin Neil • Dr Munevver Kokuer, research assistant • Barbaros Yet, PhD student • Nargis Pauran, PhD student
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EXTRA SLIDES
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Bayesian Learning and Hypothesis Testing
• Limited data and abundance of domain knowledge about many clinical subjects.
• Identifying variables and causal relations by domain knowledge.
• Bayesian Learning. • Parameter Learning with Expert
Priors.
• Bayesian Hypothesis Testing.
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Knowledge Synthesis from Models • Difficulties in using DSS models
real-time in clinical practice. • Time (e.g. entering data). • Resources (e.g. handheld devices).
• Using models to update clinical protocols. • Knowledge synthesis by BN
models.