Document Detail


Predicting the lung compliance of mechanically ventilated patients via statistical modeling.
MedLine Citation:
PMID:  22354142     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
To avoid ventilator associated lung injury (VALI) during mechanical ventilation, the ventilator is adjusted with reference to the volume distensibility or 'compliance' of the lung. For lung-protective ventilation, the lung should be inflated at its maximum compliance, i.e. when during inspiration a maximal intrapulmonary volume change is achieved by a minimal change of pressure. To accomplish this, one of the main parameters is the adjusted positive end-expiratory pressure (PEEP). As changing the ventilator settings usually produces an effect on patient's lung mechanics with a considerable time delay, the prediction of the compliance change associated with a planned change of PEEP could assist the physician at the bedside. This study introduces a machine learning approach to predict the nonlinear lung compliance for the individual patient by Gaussian processes, a probabilistic modeling technique. Experiments are based on time series data obtained from patients suffering from acute respiratory distress syndrome (ARDS). With a high hit ratio of up to 93%, the learned models could predict whether an increase/decrease of PEEP would lead to an increase/decrease of the compliance. However, the prediction of the complete pressure-volume relation for an individual patient has to be improved. We conclude that the approach is well suitable for the given problem domain but that an individualized feature selection should be applied for a precise prediction of individual pressure-volume curves.
Authors:
Steven Ganzert; Stefan Kramer; Josef Guttmann
Publication Detail:
Type:  Journal Article     Date:  2012-02-22
Journal Detail:
Title:  Physiological measurement     Volume:  33     ISSN:  1361-6579     ISO Abbreviation:  Physiol Meas     Publication Date:  2012 Mar 
Date Detail:
Created Date:  2012-02-29     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9306921     Medline TA:  Physiol Meas     Country:  England    
Other Details:
Languages:  eng     Pagination:  345-59     Citation Subset:  IM    
Affiliation:
Institut für Informatik/I12, Technische Universität München, D-85748 Garching bei München, Germany.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


Previous Document:  Patients with Nonaffective Psychosis Are at Increased Risk for Heroin Use Disorders.
Next Document:  Evaluation of fluorine-labeled gastrin-releasing peptide receptor (GRPR) agonists and antagonists by...