The design and implementation of a ventilator-management advisor

  • Authors:

  • Geoffrey W. Rutledge;George E. Thomsen;Brad R. Farr;Maria A. Tovar;Jeanette X. Polaschek;Ingo A. Beinlich;Lewis B. Sheiner;Lawrence M. Fagan

  • Affiliations:

  • Section on Medical Informatics, Medical School Office Building, Room X-215, Stanford University, Stanford, CA 94305-5479, USA;Section on Medical Informatics, Medical School Office Building, Room X-215, Stanford University, Stanford, CA 94305-5479, USA;Section on Medical Informatics, Medical School Office Building, Room X-215, Stanford University, Stanford, CA 94305-5479, USA;Section on Medical Informatics, Medical School Office Building, Room X-215, Stanford University, Stanford, CA 94305-5479, USA;Section on Medical Informatics, Medical School Office Building, Room X-215, Stanford University, Stanford, CA 94305-5479, USA;Section on Medical Informatics, Medical School Office Building, Room X-215, Stanford University, Stanford, CA 94305-5479, USA;Department of Laboratory Medicine, University of California, San Francisco, CA 94143, USA;Section on Medical Informatics, Medical School Office Building, Room X-215, Stanford University, Stanford, CA 94305-5479, USA

  • Venue:
  • Artificial Intelligence in Medicine
  • Year:
  • 1993

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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 altemative 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.