Adaptive ventilator Fi02 advisor: use of non-invasive estimations of shunt

Authors:
H. F. Kwok;D. A. Linkens;M. Mahfouf;G. H. Mills
Affiliations:
Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, UK and Surgical and Anaesthetics Sciences, University of Sheffield, K Floor, Roya ...;Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, UK;Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, UK;Surgical and Anaesthetics Sciences, University of Sheffield, K Floor, Royal Hallamshire Hospital, Glossop Road, Sheffield S10 2JF, UK
Venue:
Artificial Intelligence in Medicine
Year:
2004

Citing 2
Cited 5

Fuzzy logic alternative for analysis in the biomedical sciences

Computers and Biomedical Research
Estimation of respiratory parameters via fuzzy clustering

Artificial Intelligence in Medicine

Guest editorial: adaptive systems and hybrid computational intelligence in medicine

Artificial Intelligence in Medicine
A blood gas hybrid model for ventilated patients in ICU with new formulations for dead space and tidal volume

BioMed'06 Proceedings of the 24th IASTED international conference on Biomedical engineering
An improved blood gas intelligent hybrid model for mechanically ventilated patients in the intensive care unit

BIEN '07 Proceedings of the fifth IASTED International Conference: biomedical engineering
Predicting the impact of hospital health information technology adoption on patient satisfaction

Artificial Intelligence in Medicine
Feedback reviews and bidding in online auctions: An integrated hedonic regression and fuzzy logic expert system approach

Decision Support Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

A non-invasive and simple method of parameter estimation has been developed for the model-based decision support of the artificial ventilation in intensive care units. The parameter concerned was the respiratory shunt. Originally, the shunt had to be estimated using a numerical algorithm, which was slow and unreliable. The estimation process also required the knowledge of other parameters, whose values could only be obtained using invasive monitoring equipment. In this paper, the respiratory index is used for the shunt estimation. A linear regression model and a non-linear adaptive neuro-fuzzy inference system (ANFIS) model were used to describe the relationship between the respiratory index and the shunt. The shunts estimated using these models were then used to calculate the fractional inspired oxygen needed to attain the target arterial oxygen level of the model patient. The advisor also utilises population median values of the cardiac index and oxygen consumption index. This alleviates the need for invasive monitoring. In a simulation study, the mean squared error of the control using the ANFIS model was 0.75 kPa2 compared to 2.06 kPa2 using the linear regression model. Therefore, the performance of the FiO2 advisor was better when the shunt was estimated using the non-linear ANFIS model.