Determination of mode of ventilation using OSRE

Authors:
D. Faulke;T. A. Etchells;M. J. Harrison;P. J. G. Lisboa
Affiliations:
Department of Anaesthesia and Perioperative Medicine, Auckland City Hospital, Park Rd., Auckland, New Zealand;School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool, UK;Department of Anaesthesiology, University of Auckland, 98 Mountain Rd., Auckland, New Zealand;School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool, UK
Venue:
Computers in Biology and Medicine
Year:
2009

Citing 6
Cited 0

Ventilation mode recognition using artificial neural networks

Computers and Biomedical Research
Bias reduction in skewed binary classification with Bayesian neural networks

Neural Networks
A review of evidence of health benefit from artificial neural networks in medical intervention

Neural Networks
The evidence framework applied to classification networks

Neural Computation
The design and implementation of a ventilator-management advisor

Artificial Intelligence in Medicine
Orthogonal search-based rule extraction (OSRE) for trained neural networks: a practical and efficient approach

IEEE Transactions on Neural Networks

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Abstract

This study classifies the mode of ventilation using respiratory rate, inhaled and exhaled carbon dioxide concentrations in anaesthetised patients. Thirty seven patients were breathing spontaneously (SPONT) and 50 were on a ventilator (intermittent positive pressure ventilation, IPPV). A data-based methodology for rule inference from trained neural networks, orthogonal search-based rule extraction, identified two sets of low-order Boolean rules for differential identification of the mode of ventilation. Combining both models produced three possible outcomes; IPPV, SPONT and 'Uncertain'. The true positive rates were approximately maintained at 96% for IPPV and 93% for SPONT, with false positive rates of 0.4% for each category and 4.3% 'Uncertain' inferences.