Literature DB >> 8358487

The design and implementation of a ventilator-management advisor.

G W Rutledge1, G E Thomsen, B R Farr, M A Tovar, J X Polaschek, I A Beinlich, L B Sheiner, L M Fagan.   

Abstract

VentPlan is an implementation of the architecture developed by the qualitative-quantitative (QQ) research group for combining qualitative and quantitative computation in a ventilator-management advisor (VMA). VentPlan calculates recommended settings for four controls of a ventilator by evaluating the predicted effects of alternative ventilator settings. A belief network converts clinical diagnoses to distributions on physiologic parameters. A mathematical-modeling module applies a patient-specific mathematical model of cardiopulmonary physiology to predict the effects of alternative ventilator settings. A decision-theoretic plan evaluator ranks the predicted effects of alternative ventilator settings according to a multiattribute-value model that specifies physician preferences for ventilator treatments. Our architecture allows VentPlan to interpret quantitative observations in light of the clinical context (such as the clinical diagnosis). We report a retrospective study of the ventilator-setting changes encountered in postoperative patients in a surgical intensive-care unit (ICU). We conclude that the QQ architecture allows VentPlan to apply a patient-specific physiologic model to calculate ventilator settings that are optimal with respect to a decision-theoretic value model describing physician preferences for setting the ventilator.

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Year:  1993        PMID: 8358487     DOI: 10.1016/0933-3657(93)90006-o

Source DB:  PubMed          Journal:  Artif Intell Med        ISSN: 0933-3657            Impact factor:   5.326


  9 in total

1.  Using physiological models and decision theory for selecting appropriate ventilator settings.

Authors:  S E Rees; C Allerød; D Murley; Y Zhao; B W Smith; S Kjaergaard; P Thorgaard; S Andreassen
Journal:  J Clin Monit Comput       Date:  2006-09-15       Impact factor: 2.502

2.  A knowledge- and model-based system for automated weaning from mechanical ventilation: technical description and first clinical application.

Authors:  Dirk Schädler; Stefan Mersmann; Inéz Frerichs; Gunnar Elke; Thomas Semmel-Griebeler; Oliver Noll; Sven Pulletz; Günther Zick; Matthias David; Wolfgang Heinrichs; Jens Scholz; Norbert Weiler
Journal:  J Clin Monit Comput       Date:  2013-07-28       Impact factor: 2.502

3.  Computer-driven management of prolonged mechanical ventilation and weaning: a pilot study.

Authors:  Lila Bouadma; François Lellouche; Belen Cabello; Solenne Taillé; Jordi Mancebo; Michel Dojat; Laurent Brochard
Journal:  Intensive Care Med       Date:  2005-08-23       Impact factor: 17.440

4.  Retrospective evaluation of a decision support system for controlled mechanical ventilation.

Authors:  Dan S Karbing; Charlotte Allerød; Lars P Thomsen; Kurt Espersen; Per Thorgaard; Steen Andreassen; Søren Kjærgaard; Stephen E Rees
Journal:  Med Biol Eng Comput       Date:  2011-11-22       Impact factor: 2.602

5.  Using knowledge maintenance for preference assessment.

Authors:  N L Jain; M G Kahn
Journal:  Proc Annu Symp Comput Appl Med Care       Date:  1995

6.  Clinical evaluation of a computer-controlled pressure support mode.

Authors:  M Dojat; A Harf; D Touchard; F Lemaire; L Brochard
Journal:  Am J Respir Crit Care Med       Date:  2000-04       Impact factor: 21.405

7.  VentSim: a simulation model of cardiopulmonary physiology.

Authors:  G W Rutledge
Journal:  Proc Annu Symp Comput Appl Med Care       Date:  1994

8.  Flex: a new computerized system for mechanical ventilation.

Authors:  Fleur T Tehrani; James H Roum
Journal:  J Clin Monit Comput       Date:  2008-03-07       Impact factor: 2.502

9.  Oxygenation advisor recommends appropriate positive end expiratory pressure and FIO2 settings: retrospective validation study.

Authors:  Michael J Banner; Neil R Euliano; David Grooms; A Daniel Martin; Nawar Al-Rawas; Andrea Gabrielli
Journal:  J Clin Monit Comput       Date:  2013-10-18       Impact factor: 2.502

  9 in total

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