Identifying Causal Effects from Data for the Clinical Ventilation Process Modeling

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Han, Bin, Li, Guoliang, Leong, Tzeyun, Zhang, Yanchun, Li, Lihua, Liu, Wei, Zhu, Lei and Xu, Weidong (2008) Identifying Causal Effects from Data for the Clinical Ventilation Process Modeling. In: Proceedings of the International Conference on Biomedical Engineering and Informatics : May 27-30, 2008 , Sanya, China. Peng, Yonghong and Zhang, Yufeng, eds. IEEE Computer Society, Los Alamitos, California, pp. 517-521.


Proper modeling of the ventilation process is crucial to the effective operation of computerized ventilator management systems. We aim to develop a ventilation modeling technique, which depends less on lung dynamics assumptions, is able to describe the ventilation process quantitatively, and includes only clinically available parameters. We propose a Granger-causality based technique to identify causal relationships among ventilation variables, as the structural constraints typically provided by the subjective theory, controlled experiments or directed acyclic graphs (DAGs) are not available. We examine the performance of the proposed modeling methodology from different perspectives with real data. Domain knowledge confirmed and experiments show that the model outperforms the Vector Autoregression (VAR) and Neural Network methods. The proposed method provides initial insights into the data based ventilation process modeling. --BMEI 2008, 'BioMedical Engineering and Informatics: New Development and the Future'

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Item type Book Section
DOI 10.1109/BMEI.2008.311
Official URL
ISBN 9780769531182
Subjects Historical > FOR Classification > 0801 Artificial Intelligence and Image Processing
Historical > FOR Classification > 0807 Library and Information Studies
Historical > FOR Classification > 1103 Clinical Sciences
Historical > SEO Classification > 8903 Information Services
Historical > Faculty/School/Research Centre/Department > School of Engineering and Science
Keywords ResPubID14752, biomedical equipment, neural nets, pneumodynamics, ventilator, Granger-causality based technique, causal effects, clinical ventilation process modelling, computerised ventilator management system, directed acyclic graphs, lung dynamics, neural network, vector autoregression, biomedical computing, knowledge engineering, informatics, lungs, mathematical predictive models, mathematics
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