Confidence in neural networks: methodological issues arising from a review of safety-related applications

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Confidence in neural networksmethodological
issues arising from a review of safety-related
applications

• P.J.G. Lisboa
• p.j.lisboa_at_livjm.ac.uk

Computing and Mathematical SciencesLiverpool
John Moores University
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Outline

• Developments in commercial safety-related systems
  comprising artificial neural networks
• Increasing demand for decision support e.g.
  healthcare
• Where is the evidence of healthcare benefit from
  ANNs?
• Framework for assuring confidence in neural
  networks
• Design assurance
• Risk analysis
• Evidence of effectiveness
• Methodological issues arising from the review

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Fire alarm for office blocks

• SiemensFP11
• FirePrintTechnology
• Very high specificity

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Commercial safety-related systems

• Automotive industry
• Tow-by-wireNACT (http//www.mech.gla.ac.uk/nact
  /nact.html)
• Fuel injectionFAMIMO (http//iridia.ulb.ac.be/fa
  mimo/)
• Electronic ABSH2C (http//www.control.lth.se/H2C
  /)

Lisboa, P.J.G. Industrial use of safety-related
artificial neural networks HSE CR, 2001
http//www.hse.gov.uk/crr_pdf/crr01327.pdf
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Papnet

• Cytology screening
• FDA approvedfor secondary screening
• Proven sensitivity
• Specificityleft to user
• Cost-effective

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Epidemiology of medical error

• In the US 44,000 - 98,000 preventable deaths
  attributed to medical errors (Weingart et al, BMJ
  2000)
• Exceeds combined toll from
• motor crashes
• suicides
• falls
• poisonings
• drowning

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Epidemiology of medical error

• Managing error
• The just-world hypothesis
• Systemic approaches (Reason, BMJ 2000)

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Randomised Controlled Trials
Lisboa, P.J.G. A review of evidence of health
benefit from artificial neural networks in
medical intervention, Neural Networks, 15, 1,
9-37,2002.
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Clinical Trials
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Is there evidence of clinical benefit ?

• Clinician performance ? patient outcome
• Primary to secondary care referrals of patients
  with third molars Sens. Spec. Acc.
• 1) Control group 0.97 0.22 0.83
• 2) Paper-based clinical algorithm 0.56 0.93 0.73
• 3) MLP-based recommendation 0.56 0.79 0.67
• Which is the best performing system ?

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Is there evidence of clinical benefit ?

• Clinician performance ? patient outcome
• Primary to secondary care referrals of patients
  with third molars Sens. Spec. Acc.
• 1) Control group 0.97 0.22 0.83
• 2) Paper-based clinical algorithm 0.56 0.93 0.73
• 3) MLP-based recommendation 0.56 0.79 0.67
• 1) 1.2 2) 8.0 3) 2.7

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Performance estimation

• ROC framework
• Boost factorPPV True
  positives/Predicted positives

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Predictive models
Deficiencies in standard modelling
methods (Altman Royston, Stat. Med. 2000) 1.
Overoptimistic assessment of predictive
performance 2. Multiple regression using stepwise
variable selection 3. EPV lt 10
(samples/parameters) 4. Case-mix (cohort
variations) 5. External evaluation (protocol
changes)

• Retrospective vs. Temporal
• Prospective vs. External

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Continuum of inference models
numeric to numeric
symbolic to symbolic
numeric to symbolic
unsupervised
supervised
data driven
knowledge driven
statistical methods
signal processing
neural networks
k-means clustering
kernel methods inc. SVM
FFT
production rules
SOM/GTM
CART
logistic regression
multi-layer perceptron
control
ART
rule induction
axiomatic
wavelets
radial basis functions
independent components analysis
reinforcement learning
fuzzy logic
rule extraction
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Software life-cycle
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Risk analysis
Extract from the FDA guidelines
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The continuum of evidence(Drug development)
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The continuum of evidence(Campbell et al, 2000)
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The continuum of evidence

• Ph I Theory
• Regularisation framework
• Ph II Performance optimisation
• Complexity control
• Ph III Generalisation
• HAZOP/FMEA
• RCT case-control study
• Clinical effectiveness

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Medical Devices Directives
The continuum of evidence

• Ph I Theory
• Regularisation framework
• Ph II Performance optimisation
• Complexity control
• Ph III Generalisation
• HAZOP/FMEA
• RCT case-control study
• Clinical effectiveness

• Essential requirements
• Performance validation
• Doctrine of Substantially Equiv. Products
• Model evaluation
• Requirement for Learned Intermediaries.
• Risk assessment
• Procedure for post-marketing surveillance
• H S requirements

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Methodological issues arising

• Confidence
• Data
• Regularisation
• Calibration
• Transparency
• Rule-extraction
• Linear-in-the-parameters statistical inference
• Fuzzy or rule-based supervisory models
• Effectiveness
• Performance estimation
• Reliability
• Novelty-detection

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Generalisation
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Performance estimation

• Power calculations
• Bootstrap Ci

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Rule extraction

• Axis parallel boxes network pruning

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Rule extraction

• Axis parallel boxes network pruning

Lisboa, P.J.G., Etchells, T.A and Pountney, D.C.
Minimal MLPs do not model the XOR logic
Neurocomputing, Rapid communication, 48, 1-4,
1033-1037, 2002.
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Good practice

• Embodying a safety-culture
• Specification
• Statistically significant vs. clinically useful
• Transparency
• Verify against clinical prior knowledge
• HAZOP FMEA
• Reliability - novelty detection ?
• Maintainability - incremental learning ?

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Summary

• Assuring confidence in complex is not a specific
  issue for neural networks but applies to all
  inference systems
• A framework can be constructed based on a
  life-cycle model of safety-related software
• Good practice in the design data-based models
• Need to switch between evidence-based and
  knowledge-based cultures for v v.