Creating virtual ARDS patients

Anup Das, Mainul Haque, Marc Chikhani, Wenfei Wang, Jonathan G. Hardman, Declan G. Bates

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

This paper presents the methodology used in patient-specific calibration of a novel highly integrated model of the cardiovascular and pulmonary pathophysiology associated with Acute Respiratory Distress Syndrome (ARDS). We focus on data from previously published clinical trials on the static and dynamic cardio-pulmonary responses of three ARDS patients to changes in ventilator settings. From this data, the parameters of the integrated model were identified using an optimization-based methodology in multiple stages. Computational simulations confirm that the resulting model outputs accurately reproduce the available clinical data. Our results open up the possibility of creating in silico a biobank of virtual ARDS patients that could be used to evaluate current, and investigate novel, therapeutic strategies.

Original languageEnglish
Title of host publication2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2729-2732
Number of pages4
ISBN (Electronic)9781457702204
DOIs
Publication statusPublished - 13 Oct 2016
Externally publishedYes
Event38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States
Duration: 16 Aug 201620 Aug 2016

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume2016-October
ISSN (Print)1557-170X

Conference

Conference38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
Country/TerritoryUnited States
CityOrlando
Period16/08/1620/08/16

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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