This thesis describes the model-based development and validation of an advisor for the maintenance of artificially ventilated patients in the intensive care unit (ICU). The advisor employs fuzzy logic to represent an anaesthetist's decision making process when adjusting ventilator settings to safely maintain a patient's blood-gases and airway pressures within desired limits. Fuzzy logic was chosen for its ability to process both quantitative and qualitative data. The advisor estimates the changes in inspired O2 fraction (FI02), peak inspiratory pressure (PEEP), respiratory rate (RR), tidal volume (VT) and inspiratory time (TIN), based upon observations of the patient state and the current ventilator settings. The advisor rules only considered the ventilation of patients on volume control (VC) and pressure regulated volume control (PRVC) modes. The fuzzy rules were handcrafted using known physiological relationships and from tacit knowledge elicited during dialogue with anaesthetists. The resulting rules were validated using a computer-based model of human respiration during artificial ventilation. This model was able to simulate a wide range of patho-physiology, and using data collected from ICU it was shown that it could be matched to real clinical data to predict the patient's response to ventilator changes. Using the model, five simulated patient scenarios were constructed via discussion with an anaesthetist. These were used to test the closed-loop performance of the prototype advisor and successfully highlighted divergent behaviour in the rules. By comparing the closed-loop responses against those produced by an anaesthetist (using the patient-model), rapid rule refinement was possible. The modified advisor demonstrated better decision matching than the prototype rules, when compared against the decisions made by the anaesthetist. The modified advisor was also tested using data collected from ICU. Direct comparisons were made between the decisions given by an anaesthetist and those produced by the advisor. Good decision matching was observed in patients with well behaved physiology but soon ran into difficulties if a patients state was changing rapidly or if the patient observations contained large measurement errors.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:341868 |
| Date | January 2001 |
| Creators | Goode, Kevin Michael |
| Publisher | University of Sheffield |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://etheses.whiterose.ac.uk/14821/ |
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