On the development of intelligent medical systems for pre-operative anaesthesia assessment
Folland, Ross Simon (2005) On the development of intelligent medical systems for pre-operative anaesthesia assessment. PhD thesis, University of Warwick.
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Official URL: http://webcat.warwick.ac.uk/record=b1782756~S15
This thesis describes the research and development of a decision support tool for
determining a medical patient's suitability for surgical anaesthesia. At present,
there is a change in the way that patients are clinically assessedp rior to surgery.
The pre-operative assessment, usually conducted by a qualified anaesthetist, is
being more frequently performed by nursing grade staff. The pre-operative
assessmenet xists to minimise the risk of surgical complications for the patient.
Nursing grade staff are often not as experienced as qualified anaesthetists, and
thus are not as well suited to the role of performing the pre-operative assessment.
This research project used data collected during pre-operative assessments to
develop a decision support tool that would assist the nurse (or anaesthetist) in
determining whether a patient is suitable for surgical anaesthesia. The three
main objectives are: firstly, to research and develop an automated intelligent
systems technique for classifying heart and lung sounds and hence identifying
cardio-respiratory pathology. Secondly, to research and develop an automated
intelligent systems technique for assessing the patient's blood oxygen level and
pulse waveform. Finally, to develop a decision support tool that would combine
the assessmentsa bove in forming a decision as to whether the patient is suitable
for surgical anaesthesia.
Clinical data were collected from hospital outpatient departments and recorded
alongside the diagnoses made by a qualified anaesthetist. Heart and lung sounds
were collected using an electronic stethoscope. Using this data two ensembles of
artificial neural networks were trained to classify the different heart and lung
sounds into different pathology groups. Classification accuracies up to 99.77%
for the heart sounds, and 100% for the lung sounds has been obtained. Oxygen
saturation and pulse waveform measurements were recorded using a pulse oximeter. Using this data an artificial neural network was trained to discriminate
between normal and abnormal pulse waveforms. A discrimination accuracy of
98% has been obtained from the system. A fuzzy inference system was
generated to classify the patient's blood oxygen level as being either an
inhibiting or non-inhibiting factor in their suitability for surgical anaesthesia.
When tested the system successfully classified 100% of the test dataset. A
decision support tool, applying the genetic programming evolutionary technique
to a fuzzy classification system was created. The decision support tool combined
the results from the heart sound, lung sound and pulse oximetry classifiers in
determining whether a patient was suitable for surgical anaesthesia. The evolved
fuzzy system attained a classification accuracy of 91.79%.
The principal conclusion from this thesis is that intelligent systems, such as
artificial neural networks, genetic programming, and fuzzy inference systems,
can be successfully applied to the creation of medical decision support tools.
|Item Type:||Thesis or Dissertation (PhD)|
|Subjects:||Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
R Medicine > R Medicine (General)
|Library of Congress Subject Headings (LCSH):||Expert systems (Computer science), Decision support systems, Anesthesia, Clinical medicine -- Computer programs|
|Official Date:||January 2005|
|Institution:||University of Warwick|
|Theses Department:||School of Engineering|
|Extent:||xviii, 226 leaves|
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