Paper Info
Title
Blood Gas Predictions for Patients Under Artificial Ventilation Using Fuzzy Logic Models.
Abstract
This paper proposes a new modelling framework for accurate predictions of arterial blood gases (ABG) of the previously developed SOPAVent model (Simulation of Patients under Artificial Ventilation). Three ABG parameters which were elicited from the SOPAVent model are the partial arterial pressure of oxygen (PaO2), the partial arterial pressure of carbon-dioxide (PaCO2) and the acid-base (pH). SOPAVent generate predictions of initial ABG and predictions of ABG after ventilator settings were modified. SOPAVent's sub-models, the relative dead space (Kd) and the carbon-dioxide production (VCO2) were designed using interval type-2 fuzzy logic system (IT2FLS). Further explorations of the models were carried out using fuzzy c-means clustering (FCM) and tuning of fuzzy parameters using 'new structure' particle swarm optimization algorithm (nPSO). The new models were integrated into the SOPAVent system for blood gas predictions. SOPAVent was validated using real intensive care unit (ICU) patient data, obtained from the Royal Hallamshire Hospital and Northern General Hospital, Sheffield (UK). The prediction accuracy of SOPAVent was compared with the pre-existing SOPAVent model where the Kd and VCO2 sub-models were developed using machine learning algorithms. Significant improvements in accuracy and correlation were achieved under this frameworks for PaCO2 and pH for both the initial ABG predictions and the post ventilator settings adjustments.
Year
DOI
Venue
2017
10.1007/978-3-030-11292-9_10
Lecture Notes in Electrical Engineering
Keywords
Field
DocType
Data clustering,Interval type-2 fuzzy systems,Blood gases,Ventilator management
Particle swarm optimization,Fuzzy logic system,Artificial ventilation,Control theory,Fuzzy logic,Engineering,Dead space,Cluster analysis
Conference
Volume
ISSN
Citations 
495
1876-1100
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Siti Hazurah Indera Putera100.34
Mahdi Mahfouf223533.17
Gary H. Mills352.84