3 research outputs found
INTELLIGENT TECHNIQUES FOR HANDLING UNCERTAINTY IN THE ASSESSMENT OF NEONATAL OUTCOME
Objective assessment of the neonatal outcome of labour is important, but it is a difficult
and challenging problem. It is an invaluable source of information which can be used to
provide feedback to clinicians, to audit a unit's overall performance, and can guide subsequent
neonatal care. Current methods are inadequate as they fail to distinguish damage that
occurred during labour from damage that occurred before or after labour. Analysis of the
chemical acid-base status of blood taken from the umbilical cord of an infant immediately
after delivery provides information on any damage suffered by the infant due to lack of oxygen
during labour. However, this process is complex and error prone, and requires expertise
which is not always available on labour wards.
A model of clinical expertise required for the accurate interpretation of umbilical acid-base
status was developed, and encapsulated in a rule-based expert system. This expert system
checks results to ensure their consistency, identifies whether the results come from arterial
or venous vessels, and then produces an interpretation of their meaning. This 'crisp' expert
system was validated, verified and commercially released, and has since been installed at
twenty two hospitals all around the United Kingdom.
The assessment of umbilical acid-base status is characterised by uncertainty in both the basic
data and the knowledge required for its interpretation. Fuzzy logic provides a technique
for representing both these forms of uncertainty in a single framework. A 'preliminary'
fuzzy-logic based expert system to interpret error-free results was developed, based on the
knowledge embedded in the crisp expert system. Its performance was compared against clinicians
in a validation test, but initially its performance was found to be poor in comparison
with the clinicians and inferior to the crisp expert system. An automatic tuning algorithm
was developed to modify the behaviour of the fuzzy model utilised in the expert system.
Sub-normal membership functions were used to weight terms in the fuzzy expert system in
a novel manner. This resulted in an improvement in the performance of the fuzzy expert
system to a level comparable to the clinicians, and superior to the crisp expert system.
Experimental work was carried out to evaluate the imprecision in umbilical cord acid-base
parameters. This information, in conjunction with fresh knowledge elicitation sessions, allowed
the creation of a more comprehensive fuzzy expert system, to validate and interpret
all acid-base data. This 'integrated' fuzzy expert system was tuned using the comparison
data obtained previously, and incorporated vessel identification rules and interpretation rules,
with numeric and linguistic outputs for each. The performance of each of the outputs was
evaluated in a rigorous validation study. This demonstrated excellent agreement with the
experts for the numeric outputs, and agreement on a par with the experts for the linguistic
outputs. The numeric interpretation produced by the fuzzy expert system is a novel single
dimensional measure that accurately represents the severity of acid-base results.
The development of the crisp and fuzzy expert systems represents a major achievement and
constitutes a significant contribution to the assessment of neonatal outcome.Plymouth Postgraduate Medical Schoo
Integrative risk-based assessment modelling of safety-critical marine and offshore applications
This research has first reviewed the current status and future aspects of marine and offshore safety assessment. The major problems identified in marine and offshore safety assessment in this research are associated with inappropriate treatment of uncertainty in data and human error issues during the modelling process. Following the identification of the research needs, this thesis has developed several analytical models for the safety assessment of marine and offshore systems/units. Such models can be effectively integrated into a risk-based framework using the marine formal safety assessment and offshore safety case concepts. Bayesian network (BN) and fuzzy logic (FL) approaches applicable to marine and offshore safety assessment have been proposed for systematically and effectively addressing uncertainty due to randomness and vagueness in data respectively. BN test cases for both a ship evacuation process and a collision scenario between the shuttle tanker and Floating, Production, Storage and Offloading unit (FPSO) have been produced within a cause-effect domain in which Bayes' theorem is the focal mechanism of inference processing. The proposed FL model incorporating fuzzy set theory and an evidential reasoning synthesis has been demonstrated on the FPSO-shuttle tanker collision scenario. The FL and BN models have been combined via mass assignment theory into a fuzzy-Bayesian network (FBN) in which the advantages of both are incorporated. This FBN model has then been demonstrated by addressing human error issues in a ship evacuation study using performance-shaping factors. It is concluded that the developed FL, BN and FBN models provide a flexible and transparent way of improving safety knowledge, assessments and practices in the marine and offshore applications. The outcomes have the potential to facilitate the decision-making process in a risk-based framework. Finally, the results of the research are summarised and areas where further research is required to improve the developed methodologies are outline