KSL-93-34
## Dynamic Selection of Models for a Ventilator-Management Advisor

**Reference: **
Rutledge, G. Dynamic Selection of Models for a Ventilator-Management Advisor. Washington, D.C, 1993.

**Abstract:** A ventilator-management advisor (VMA) is a computer program
that monitors patients who are treated with a mechanical
ventilator. A VMA implements a patient-specific physiologic
model to interpret patient data and to predict the effects of
alternative control settings for the ventilator. Because a
VMA evaluates its physiologic model repeatedly during each
cycle of data interpretation, highly complex models may
require more computation time than is available in this time-
critical application. On the other hand, less complex models
may be inaccurate if they are unable to represent a patient's
physiologic abnormalities. For each patient, a VMA should
select a model that balances the tradeoff of prediction
accuracy and computation-time complexity. I present a method
to select models that are at an appropriate level of detail
for time-constrained decision tasks. The method is based on a
local search in a graph of models (GoM) for a minimally
complex model that is likely to make accurate predictions.
For each model under consideration, a belief network computes
a probability of model adequacy given the qualitative "prior"
information, and the fit of the model to the data provides a
measure of the conditional probability of adequacy given the
quantitative observations. I apply this method to the
problem of model selection for a VMA. I describe an
implementation of a graph of physiologic models that range in
complexity from VentPlan, a simple model with 3-compartments,
to VentSim, a multicompartment model with detailed airway,
circulation and mechanical ventilator components. I
demonstrate how this model-selection method selects
dynamically, from this GoM, models t

**Notes:** September.

Full paper available as ps.

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